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HyperAI Super Neural
HyperAI Super Neural
Feb 13, 2026 · Artificial Intelligence

UCL Team Uses Federated Learning to Train Blood Morphology Models Without Sharing Data

A UCL computer‑science team presents a federated learning framework for white‑blood‑cell morphology analysis that preserves patient privacy, leverages heterogeneous clinical slide data from multiple sites, and achieves superior cross‑site performance and generalisation to unseen institutions compared with centralized training.

Blood MorphologyDINOv2Federated Learning
0 likes · 14 min read
UCL Team Uses Federated Learning to Train Blood Morphology Models Without Sharing Data
Alimama Tech
Alimama Tech
Sep 24, 2025 · Information Security

Differential Privacy Explained: Theory, Techniques, and Real-World AI Deployments

This article provides a comprehensive overview of differential privacy, covering its mathematical foundations, evolution from theory to engineering, classification of privacy mechanisms, practical implementation cases such as Alibaba's Secure Data Hub, and diverse application scenarios across healthcare, finance, location analytics, and energy forecasting.

AI complianceFederated LearningNoise Mechanisms
0 likes · 23 min read
Differential Privacy Explained: Theory, Techniques, and Real-World AI Deployments
Data Party THU
Data Party THU
Sep 22, 2025 · Artificial Intelligence

How to Secure Large‑Model Training: Practical Techniques and Real‑World Cases

This article systematically examines the major security challenges of large‑model training—including data leakage, adversarial attacks, bias, and supply‑chain risks—and presents concrete solutions such as differential privacy, federated learning, adversarial training, backdoor detection, and lifecycle protection to guide practitioners toward safer AI deployments.

AI SafetyFederated Learningadversarial training
0 likes · 14 min read
How to Secure Large‑Model Training: Practical Techniques and Real‑World Cases
Alimama Tech
Alimama Tech
Sep 17, 2025 · Artificial Intelligence

How Federated Learning Balances Privacy and Collaboration in AI

Federated Learning enables multiple parties to collaboratively train a global AI model without sharing raw data, using techniques like local training, encrypted parameter exchange, and secure aggregation, while addressing privacy, communication efficiency, heterogeneity, and incentive challenges across horizontal, vertical, and transfer learning scenarios.

Federated LearningHorizontal FLSecure Aggregation
0 likes · 24 min read
How Federated Learning Balances Privacy and Collaboration in AI
Alimama Tech
Alimama Tech
Sep 3, 2025 · Artificial Intelligence

Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality

Privacy-Preserving Machine Learning (PPML) integrates cryptographic techniques such as federated learning, differential privacy, homomorphic encryption, and secure multi-party computation to enable model training and inference on encrypted or distributed data, thereby breaking data silos while safeguarding privacy across sectors like healthcare, finance, and advertising.

Federated LearningHomomorphic Encryptionmachine learning
0 likes · 18 min read
Privacy-Preserving Machine Learning: Balancing Data Utility and Confidentiality
Alimama Tech
Alimama Tech
Aug 6, 2025 · Information Security

How Privacy-Enhancing Technologies Are Revolutionizing Data Use in Digital Advertising

This article reviews the background, core techniques, and typical applications of privacy‑enhancing technologies—including secure multi‑party computation, privacy‑preserving machine learning, differential privacy, and trusted execution environments—highlighting their role in unlocking multi‑party data value while ensuring compliance and privacy protection.

Federated LearningPrivacy Computingdifferential privacy
0 likes · 20 min read
How Privacy-Enhancing Technologies Are Revolutionizing Data Use in Digital Advertising
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
May 19, 2025 · Artificial Intelligence

How WASP Generates High‑Quality DP Synthetic Data with Multi‑Model Collaboration

WASP is a privacy‑preserving framework that fuses multiple pretrained language models through a weighted Top‑Q voting scheme to synthesize differential‑private data, dramatically improving downstream task performance even when only a few private samples are available, and it scales to federated settings.

Federated LearningMulti-Model Fusiondifferential privacy
0 likes · 19 min read
How WASP Generates High‑Quality DP Synthetic Data with Multi‑Model Collaboration
DataFunTalk
DataFunTalk
May 2, 2025 · Artificial Intelligence

SecretFlow Meetup Chengdu – Privacy Computing Innovation and Application Practice

The SecretFlow open‑source community and Chengdu Smart City Research Institute co‑host a deep‑tech meetup in Chengdu, featuring leading experts from Ant Group, UESTC, and industry partners who will explore privacy‑computing principles, SPU technology, federated learning, and real‑world financial and governmental use cases through talks and hands‑on workshops.

AIFederated LearningPrivacy Computing
0 likes · 8 min read
SecretFlow Meetup Chengdu – Privacy Computing Innovation and Application Practice
Cognitive Technology Team
Cognitive Technology Team
Feb 7, 2025 · Artificial Intelligence

Knowledge Distillation: Concepts, Techniques, Applications, and Future Directions

This article explains knowledge distillation—a technique introduced by Geoffrey Hinton that transfers knowledge from large teacher models to compact student models—covering its core concepts, loss functions, various distillation strategies, notable applications in edge computing, federated learning, continual learning, and emerging research directions.

Deep LearningEdge ComputingFederated Learning
0 likes · 7 min read
Knowledge Distillation: Concepts, Techniques, Applications, and Future Directions
DataFunTalk
DataFunTalk
Nov 17, 2024 · Artificial Intelligence

Federated Learning and Data Security in the Era of Large Models: Research Overview and the FLAIR Platform

This presentation reviews recent research on data security and utilization in the large‑model era, covering privacy‑preserving federated learning, knowledge‑transfer techniques, prototype‑based modeling, multi‑model fusion methods such as FuseGen, and introduces the federated knowledge computing platform FLAIR for both horizontal and vertical federated scenarios.

FLAIRFederated LearningKnowledge Transfer
0 likes · 19 min read
Federated Learning and Data Security in the Era of Large Models: Research Overview and the FLAIR Platform
Tencent Advertising Technology
Tencent Advertising Technology
Oct 23, 2024 · Artificial Intelligence

FedMix: Boosting Vertical Federated Learning with Data Mixture

This paper introduces FedMix, a method that enhances vertical federated learning by mixing aligned and unaligned data, theoretically demonstrating the value of unaligned data and empirically achieving over 10% ROI improvement and significant AUC gains while keeping computational and communication overhead low.

Federated Learningdata mixtureprivacy
0 likes · 11 min read
FedMix: Boosting Vertical Federated Learning with Data Mixture
Rare Earth Juejin Tech Community
Rare Earth Juejin Tech Community
May 9, 2024 · Artificial Intelligence

On‑Device AI and Federated Learning: Era Background, Theory, and Practical Applications

This article outlines the evolution from 1G to 6G communications, explains the third AI wave driven by big data, theory, and compute, introduces federated learning (horizontal, vertical, transfer), and details on‑device AI architectures, decision tree and neural network models, and real‑world use cases such as video preloading and autonomous driving.

Big DataEdge ComputingFederated Learning
0 likes · 13 min read
On‑Device AI and Federated Learning: Era Background, Theory, and Practical Applications
Alimama Tech
Alimama Tech
Feb 29, 2024 · Industry Insights

How Alibaba’s Secure Data Hub Powers Cross‑Domain Advertising Tracking with Privacy‑Preserving Computation

This article details how Alibaba Mama’s Secure Data Hub (SDH) leverages multi‑party computation, federated learning, and differential privacy to break data silos in advertising, enabling secure cross‑domain user tracking, full‑domain asset analysis, and rapid, privacy‑compliant marketing insights.

Federated LearningMPCPrivacy Computing
0 likes · 18 min read
How Alibaba’s Secure Data Hub Powers Cross‑Domain Advertising Tracking with Privacy‑Preserving Computation
NewBeeNLP
NewBeeNLP
Feb 7, 2024 · Artificial Intelligence

On‑Device Recommendation Systems: Inference, Training, and Privacy Explained

This article reviews the latest progress in on‑device recommendation systems, detailing lightweight inference and deployment techniques, on‑device training and update strategies—including federated and distributed approaches—as well as security and privacy challenges, and outlines open research directions for this emerging AI paradigm.

AIEdge ComputingFederated Learning
0 likes · 10 min read
On‑Device Recommendation Systems: Inference, Training, and Privacy Explained
Alimama Tech
Alimama Tech
Dec 21, 2023 · Information Security

Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising

Alibaba Mama’s Secure Data Hub delivers a privacy‑enhanced clean‑room for advertising by combining multi‑party computation, federated learning and differential privacy with encrypted operators on a Flink engine, offering cloud‑agnostic, scalable deployment that enables cross‑domain analytics while protecting raw user data and boosting ROI.

Federated LearningPrivacy Computingadvertising analytics
0 likes · 13 min read
Alibaba Mama Secure Data Hub: Cloud Architecture and Privacy-Preserving Advertising
vivo Internet Technology
vivo Internet Technology
Aug 23, 2023 · Artificial Intelligence

Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands

Federated learning enables multiple organizations to jointly train high‑performing AI models without sharing raw data, using techniques such as secure multi‑party computation, differential privacy, and homomorphic encryption, thereby overcoming data‑island and regulatory constraints and supporting applications in mobile edge AI, finance, retail, and healthcare.

Data IslandFederated LearningHomomorphic Encryption
0 likes · 19 min read
Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands
AntTech
AntTech
Aug 15, 2023 · Information Security

VILLAIN: Backdoor Attacks Against Vertical Split Learning Presented at USENIX Security 2023

The paper "VILLAIN: Backdoor Attacks Against Vertical Split Learning" introduced at USENIX Security 2023 proposes a novel framework that enables label‑free attackers to infer data labels and inject backdoors into vertically partitioned federated learning models, highlighting new security challenges and defense considerations for collaborative AI systems.

Federated LearningUSENIX Securitybackdoor attack
0 likes · 4 min read
VILLAIN: Backdoor Attacks Against Vertical Split Learning Presented at USENIX Security 2023
DataFunTalk
DataFunTalk
Jul 19, 2023 · Artificial Intelligence

Privacy Computing Practices at China Construction Bank: From External Data Use to an Enterprise‑Grade Platform

This article details China Construction Bank's journey in privacy computing, covering the history of external data usage, early federated learning experiments, rapid demand growth, the construction of an enterprise‑grade privacy computing platform, its design principles, achievements, and future directions.

AIFederated LearningPrivacy Computing
0 likes · 14 min read
Privacy Computing Practices at China Construction Bank: From External Data Use to an Enterprise‑Grade Platform
Network Intelligence Research Center (NIRC)
Network Intelligence Research Center (NIRC)
Jul 4, 2023 · Artificial Intelligence

FedMRL: Federated Meta Reinforcement Learning for Cold-Start Slice Resource Management

FedMRL tackles the cold‑start problem of network‑slice resource orchestration by combining federated learning with meta‑reinforcement learning, using a two‑loop training process that preserves SP data privacy and consistently outperforms TUNE, TDSC, and IOSP across diverse 6G network conditions.

6GFederated LearningMAML
0 likes · 6 min read
FedMRL: Federated Meta Reinforcement Learning for Cold-Start Slice Resource Management
Meituan Technology Team
Meituan Technology Team
Jun 15, 2023 · Artificial Intelligence

Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights

This article reviews eight CVPR 2023 papers selected by Meituan’s technology team, covering self‑supervised learning, domain adaptation, federated learning, object detection, 3D reconstruction, GAN‑based pre‑training, RGB‑T tracking, vision‑language navigation, and visual‑textual layout generation, highlighting each work’s methodology, experiments, and reported performance gains.

3D Object DetectionCVPR 2023Computer Vision
0 likes · 15 min read
Meituan Technical Team's 8 CVPR 2023 Papers: Overview and Insights
WeChat Backend Team
WeChat Backend Team
Jun 13, 2023 · Artificial Intelligence

Boosting Vertical Federated Learning: Optimizing Paillier Encryption & Model Stability

This article examines the challenges of data privacy in big‑data environments and presents a comprehensive approach to vertical federated learning, detailing framework optimizations, Paillier homomorphic encryption enhancements, PSI‑based feature selection, and adversarial learning techniques to improve model stability and deployment on a unified ML platform.

Federated LearningPaillier encryptionPrivacy Computing
0 likes · 19 min read
Boosting Vertical Federated Learning: Optimizing Paillier Encryption & Model Stability
AntTech
AntTech
May 4, 2023 · Artificial Intelligence

Privacy Risks and Differentially Private Defense for Federated Knowledge Graph Representation Learning

This paper investigates the privacy leakage risks of federated knowledge graph representation learning, designs three membership inference attacks to quantify the threats, and proposes DP‑Flames, a differential‑privacy‑based defense that leverages gradient sparsity to achieve a favorable privacy‑utility trade‑off.

DP-FlamesFederated Learningdifferential privacy
0 likes · 15 min read
Privacy Risks and Differentially Private Defense for Federated Knowledge Graph Representation Learning
DataFunSummit
DataFunSummit
Apr 27, 2023 · Artificial Intelligence

Baidu's Interoperability Solutions for Federated Learning: Principles, JinKe Alliance, and the Open‑Source HIGHFLIP Protocol

The article presents Baidu's comprehensive approach to federated‑learning interoperability, covering the underlying principles, the JinKe Alliance bottom‑layer solution, the high‑level HIGHFLIP protocol, and a comparative discussion of white‑box, gray‑box, and black‑box integration strategies.

AI InfrastructureBaiduFederated Learning
0 likes · 11 min read
Baidu's Interoperability Solutions for Federated Learning: Principles, JinKe Alliance, and the Open‑Source HIGHFLIP Protocol
DataFunSummit
DataFunSummit
Feb 12, 2023 · Information Security

Privacy Computing: Technical Routes Overview and Ant Group’s Contributions

This article introduces and compares major privacy computing technologies—including MPC, federated learning, TEE, and proxy MPC—evaluating them across security, development cost, operational cost, accuracy, performance, participant scale, control, hardware cost, and trust, and then outlines Ant Group’s privacy computing framework, applications, and standards work.

Ant GroupFederated LearningMPC
0 likes · 8 min read
Privacy Computing: Technical Routes Overview and Ant Group’s Contributions
DataFunSummit
DataFunSummit
Dec 27, 2022 · Blockchain

Financial Technology Testing Facilitates Secure Data Sharing in Finance

The article explains how emerging collaboration models, privacy‑computing and blockchain technologies, together with evolving regulations and industry standards, enable secure and compliant financial data sharing, describing the technical foundations, evaluation criteria, and practical testing practices that support trustworthy FinTech products.

BlockchainFederated LearningFinTech standards
0 likes · 22 min read
Financial Technology Testing Facilitates Secure Data Sharing in Finance
DataFunSummit
DataFunSummit
Dec 26, 2022 · Information Security

Privacy Computing in Digital Government: Background, Technical Roadmap, Case Studies, Challenges, and Recommendations

This article introduces the background, technical roadmap, real-world cases, implementation challenges, and suggested approaches for privacy computing in digital government, highlighting how secure multi‑party computation, federated learning, and trusted execution environments can enable safe data sharing.

Federated LearningPrivacy Computingdata security
0 likes · 18 min read
Privacy Computing in Digital Government: Background, Technical Roadmap, Case Studies, Challenges, and Recommendations
DataFunTalk
DataFunTalk
Dec 21, 2022 · Artificial Intelligence

A Comprehensive Overview of Computational Advertising: Architecture, Deep‑Learning Evolution, and Future Directions

This article provides a thorough examination of computational advertising, covering the oCPM pricing model as a superset, classic system architecture, the evolution of core modules such as ad ranking, pacing, bidding, federated learning, calibration, and conversion‑delay handling, and concludes with career advice for algorithm engineers.

CalibrationDeep LearningFederated Learning
0 likes · 29 min read
A Comprehensive Overview of Computational Advertising: Architecture, Deep‑Learning Evolution, and Future Directions
DataFunSummit
DataFunSummit
Dec 18, 2022 · Information Security

Privacy Computing: Concepts, Product Architecture, and Medical Industry Applications by Ant Group

This article explains Ant Group's privacy computing framework, covering its fundamental concepts, layered product architecture, and four concrete use‑cases in the medical sector—including insurance, hospitals, health commissions, and medical device manufacturers—demonstrating how secure multi‑party computation and federated learning enable data collaboration while preserving privacy.

Ant GroupFederated LearningPrivacy Computing
0 likes · 12 min read
Privacy Computing: Concepts, Product Architecture, and Medical Industry Applications by Ant Group
DataFunSummit
DataFunSummit
Oct 8, 2022 · Information Security

Exploring Privacy Computing Technologies in the Open Financial Ecosystem

This article provides a comprehensive overview of privacy computing—covering its background, key techniques such as MPC, TEE, federated learning, homomorphic encryption, and differential privacy—and examines how these technologies are applied in open financial ecosystems, including use cases, challenges, and future directions.

BlockchainFederated LearningPrivacy Computing
0 likes · 25 min read
Exploring Privacy Computing Technologies in the Open Financial Ecosystem
DataFunSummit
DataFunSummit
Oct 4, 2022 · Artificial Intelligence

Graph Federated Learning: Necessity, Classification, Algorithms, Platform Architecture, and Financial Applications

This article provides a comprehensive overview of graph federated learning, covering its motivation, taxonomy, representative algorithms, platform design, practical financial use cases, and future research challenges, with a focus on privacy-preserving distributed graph neural network training.

AIFederated LearningFinancial Applications
0 likes · 15 min read
Graph Federated Learning: Necessity, Classification, Algorithms, Platform Architecture, and Financial Applications
AntTech
AntTech
Sep 29, 2022 · Artificial Intelligence

Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification

This article presents VFGNN, a privacy‑preserving vertical federated graph neural network designed for node classification, detailing its architecture, differential‑privacy enhancements, and experimental results that demonstrate superior accuracy over single‑party baselines across multiple graph datasets.

Federated LearningVertical Partitiondifferential privacy
0 likes · 14 min read
Privacy-Preserving Vertical Federated Graph Neural Network for Node Classification
DataFunSummit
DataFunSummit
Sep 22, 2022 · Information Security

Privacy Computing Enables Secure Medical Data Sharing and Analysis

This presentation introduces how privacy‑preserving computation technologies such as federated learning, trusted execution environments, and cryptographic methods empower the secure flow, analysis, and value extraction of large‑scale medical health data while addressing de‑identification risks and regulatory constraints.

AIFederated LearningPrivacy Computing
0 likes · 19 min read
Privacy Computing Enables Secure Medical Data Sharing and Analysis
AntTech
AntTech
Sep 22, 2022 · Artificial Intelligence

SecretFlow Open‑Source Privacy Computing Framework Releases Version 0.7 with Enhanced MPC, Federated Learning, and Performance Optimizations

The SecretFlow privacy‑computing open‑source framework announced its inclusion in the PPCA Open‑Source Working Group and launched version 0.7, adding multi‑party computation, federated learning, infrastructure upgrades, and documentation improvements to advance secure AI and data analytics.

AIFederated LearningMPC
0 likes · 7 min read
SecretFlow Open‑Source Privacy Computing Framework Releases Version 0.7 with Enhanced MPC, Federated Learning, and Performance Optimizations
DataFunSummit
DataFunSummit
Sep 19, 2022 · Artificial Intelligence

Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview

This article reviews the rapid development of privacy-preserving computation, explains its classification, discusses differential privacy, secure multi‑party computation, federated and split learning, and demonstrates how these techniques can be combined for graph learning and recommendation systems, culminating in a description of the JinZhiTa privacy‑computing platform.

Federated LearningPrivacy Computinggraph learning
0 likes · 20 min read
Privacy-Preserving Graph Learning and Recommendation: Techniques, Challenges, and Platform Overview
DataFunSummit
DataFunSummit
Aug 7, 2022 · Artificial Intelligence

Vertical Federated Learning: Characteristics, Research Directions, and Performance Optimization

This article introduces federated learning, traces its evolution, compares horizontal and vertical federated learning, analyzes the unique computational traits of vertical FL, and presents practical performance‑optimization techniques such as offline computation, sparse‑data handling, communication compression, and homomorphic encryption integration.

Federated LearningPerformance OptimizationPrivacy Computing
0 likes · 13 min read
Vertical Federated Learning: Characteristics, Research Directions, and Performance Optimization
ByteDance Terminal Technology
ByteDance Terminal Technology
Jul 29, 2022 · Artificial Intelligence

Pitaya: ByteDance’s End‑Side AI Engineering Platform Overview

Pitaya, built by ByteDance’s Client AI and MLX teams, is a comprehensive end‑side AI engineering platform that provides a full workflow from model development and data preparation to deployment, monitoring, and federated learning, supporting large‑scale commercial scenarios across multiple apps.

AI PlatformFederated LearningInference Engine
0 likes · 14 min read
Pitaya: ByteDance’s End‑Side AI Engineering Platform Overview
DataFunTalk
DataFunTalk
Jul 8, 2022 · Information Security

DataFun 2022 Summit on Privacy Computing and Data Security

DataFun's 2022 summit brings together leading experts from academia and industry to discuss privacy computing, federated learning, secure data sharing, and their applications across finance, healthcare, telecom, and blockchain, offering insights into technologies, standards, and real-world implementations that enable data utility while protecting privacy.

Big DataFederated LearningPrivacy Computing
0 likes · 43 min read
DataFun 2022 Summit on Privacy Computing and Data Security
AntTech
AntTech
Jun 16, 2022 · Information Security

Privacy Computing: How Digital Technologies Address Privacy Protection Pain Points

This article examines the rapid growth of privacy computing in China, outlining policy and market drivers, explaining key technologies such as secure multiparty computation, trusted execution environments, homomorphic encryption, differential privacy and federated learning, and discussing the legal, technical and ecosystem challenges that hinder its wider adoption.

Federated LearningHomomorphic EncryptionPrivacy Computing
0 likes · 11 min read
Privacy Computing: How Digital Technologies Address Privacy Protection Pain Points
Alimama Tech
Alimama Tech
May 25, 2022 · Artificial Intelligence

AI‑Driven Solutions for External Advertising Effectiveness at Alibaba Mama

Alibaba Mama boosts external-media advertising ROI by deploying AI-driven models—privacy-preserving federated learning, hierarchical representation integration, uncertainty-regularized knowledge distillation, and calibrated DNNs—to overcome missing user-preference data, sparse post-click conversions, sample-selection bias, and probability-calibration challenges.

AIFederated Learningconversion rate prediction
0 likes · 13 min read
AI‑Driven Solutions for External Advertising Effectiveness at Alibaba Mama
AntTech
AntTech
Apr 8, 2022 · Artificial Intelligence

Release of Financial Application Guidance for Multi‑Party Secure Computation and Federated Learning

On March 29, the Beijing FinTech Industry Alliance published two white‑papers—‘Multi‑Party Secure Computation Financial Application Status and Implementation Guide’ and ‘Federated Learning Technology Financial Application White Paper’—detailing policies, standards, case studies, and recommendations for deploying privacy‑preserving AI technologies in the financial sector.

AIFederated LearningMPC
0 likes · 4 min read
Release of Financial Application Guidance for Multi‑Party Secure Computation and Federated Learning
DataFunTalk
DataFunTalk
Apr 3, 2022 · Artificial Intelligence

Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning

This article presents a comprehensive overview of QQ Music's recall system, detailing business scenarios, challenges such as noisy user behavior and cold‑start, and four key solutions—including knowledge‑graph‑enhanced recall, sequence and multi‑interest modeling, audio‑based recall, and federated learning—along with experimental results, deployment details, and a Q&A session.

Audio EmbeddingFederated Learningmulti‑interest
0 likes · 20 min read
Exploring QQ Music Recall Algorithms: Knowledge‑Graph Fusion, Sequence & Multi‑Interest Modeling, Audio Recall, and Federated Learning
DataFunTalk
DataFunTalk
Dec 21, 2021 · Artificial Intelligence

Personalized Federated Learning and AI for Drug Discovery: Challenges, Applications, and Cloud Solutions

This talk by Huawei senior engineer Xu Chi explores the challenges of drug screening, AI-driven drug discovery practices, and how personalized federated learning combined with Huawei Cloud's high‑performance computing accelerates pharmaceutical research, including case studies, platform services, and collaborative efforts.

AIBig DataFederated Learning
0 likes · 11 min read
Personalized Federated Learning and AI for Drug Discovery: Challenges, Applications, and Cloud Solutions
DataFunSummit
DataFunSummit
Dec 19, 2021 · Artificial Intelligence

Personalized Federated Learning and AI for Accelerating Drug Discovery

The talk by Huawei senior engineer Xu Chi explores the challenges of drug screening, recent AI-driven advances in pharmaceutical research, and how personalized federated learning on Huawei Cloud accelerates drug discovery, including case studies, platform architecture, and large-scale virtual screening services.

AIFederated Learningbiomedical AI
0 likes · 10 min read
Personalized Federated Learning and AI for Accelerating Drug Discovery
AntTech
AntTech
Nov 19, 2021 · Information Security

Privacy Computing: Current Applications and Challenges in Finance, Healthcare, and Government

The 2021 China Internet Law Conference report shows that privacy‑preserving computation has begun pilot deployments in finance, medical and public‑sector domains, delivering fraud‑risk reduction, secure biomedical research, and collaborative power‑grid analytics, yet it remains in early stages and faces scalability, standards, and policy challenges.

Federated LearningPrivacy Computingdata security
0 likes · 7 min read
Privacy Computing: Current Applications and Challenges in Finance, Healthcare, and Government
DataFunTalk
DataFunTalk
Nov 7, 2021 · Artificial Intelligence

Federated Learning for Financial Data Sharing: Compliance, Incentives, and Technical Innovations

This presentation explains how federated learning can meet new regulatory requirements for financial data sharing by introducing audit‑enabled models, incentive mechanisms, blockchain‑based data marketplaces, and a Federated AI Hub that together address compliance, security, and practical deployment challenges in the finance sector.

BlockchainData IncentivesFederated Learning
0 likes · 10 min read
Federated Learning for Financial Data Sharing: Compliance, Incentives, and Technical Innovations
DataFunSummit
DataFunSummit
Nov 6, 2021 · Artificial Intelligence

Federated Learning for Financial Data Sharing: Compliance, Auditable Solutions, and Blockchain‑Enabled Marketplace

This article presents a comprehensive overview of how federated learning can be applied to financial data sharing, covering regulatory pressures, audit‑ready blockchain solutions, privacy‑preserving PSI techniques, incentive‑driven data marketplaces, and future directions for secure, compliant AI deployment in the finance sector.

AIBlockchainFederated Learning
0 likes · 10 min read
Federated Learning for Financial Data Sharing: Compliance, Auditable Solutions, and Blockchain‑Enabled Marketplace
DataFunTalk
DataFunTalk
Nov 6, 2021 · Artificial Intelligence

Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation

The article introduces Alibaba's Elastic Federated Learning Solution (EFLS), describing its business motivations, core functionalities, system architecture, sample‑set intersection, federated training pipeline, novel algorithms, product console, and future roadmap for privacy‑preserving advertising in large‑scale sparse scenarios.

AdvertisingDistributed SystemsFederated Learning
0 likes · 18 min read
Elastic Federated Learning Solution (EFLS): Project Overview, Architecture, and Technical Implementation
DataFunSummit
DataFunSummit
Oct 23, 2021 · Artificial Intelligence

Privacy Computing: The Federated Learning Three‑Part FIRM Architecture and Its Industrial Applications

This article introduces the background of privacy computing, explains the three‑stage FIRM reference architecture for federated learning, describes key technologies such as the Ionic Bond communication framework and HeteroDeepFM, and showcases real‑world applications in marketing, risk control, and government sectors.

AI securityData CollaborationFIRM architecture
0 likes · 17 min read
Privacy Computing: The Federated Learning Three‑Part FIRM Architecture and Its Industrial Applications
DataFunTalk
DataFunTalk
Oct 2, 2021 · Artificial Intelligence

Baidu Data Federation Platform: Architecture, Applications, Federated Learning, and Explainability

This article presents an in‑depth overview of Baidu's Data Federation Platform, detailing its layered architecture, core technical capabilities, privacy‑preserving collaborative research on epidemic prediction and shared vehicle optimization, and explores federated learning types, PaddleFL implementations, and model explainability techniques.

Big DataFederated Learningexplainability
0 likes · 22 min read
Baidu Data Federation Platform: Architecture, Applications, Federated Learning, and Explainability
DataFunTalk
DataFunTalk
Aug 24, 2021 · Artificial Intelligence

De‑identification in Federated Learning: Using xID Technology to Protect Sample Intersection Information

This article explains federated learning, its vertical and horizontal variants, the privacy risks of sample intersection in financial scenarios, reviews common de‑identification methods, and introduces the xID technique with generation‑mapping services to securely protect intersecting data while enabling collaborative AI modeling.

Data De-identificationFederated LearningVertical FL
0 likes · 13 min read
De‑identification in Federated Learning: Using xID Technology to Protect Sample Intersection Information
AntTech
AntTech
Jun 25, 2021 · Information Security

2021 WAIC Privacy Computing Academic Exchange – Overview and Schedule

The 2021 World Artificial Intelligence Conference (WAIC) in Shanghai hosts a Privacy Computing Academic Exchange on July 8, featuring keynote speeches, panel discussions, paper and poster sessions on secure multi‑party computation, federated learning, and differential privacy, with calls for submissions and a detailed agenda.

AI ConferenceFederated LearningPrivacy Computing
0 likes · 6 min read
2021 WAIC Privacy Computing Academic Exchange – Overview and Schedule
Baidu Geek Talk
Baidu Geek Talk
Jun 2, 2021 · Industry Insights

How Federated Computing Secures Data While Powering AI: Core Techniques Explained

This article provides a concise technical overview of federated computing, covering its origins, core cryptographic methods such as MPC, garbled circuits, secret sharing, homomorphic encryption, and TEE, and explains how Baidu applies these technologies to enable privacy‑preserving AI in advertising and other industries.

AIFederated Learningdata privacy
0 likes · 12 min read
How Federated Computing Secures Data While Powering AI: Core Techniques Explained
DataFunTalk
DataFunTalk
May 28, 2021 · Artificial Intelligence

JD's Open‑Source Federated Learning Solution 9N‑FL: Architecture, Features, Timeline, and Business Impact

This article introduces JD's open‑source federated learning platform 9N‑FL, explaining the data‑island problem, the fundamentals and classifications of federated learning, its four key features, the system’s layered architecture, development timeline, real‑world advertising use case results, and future enhancements.

9N-FLBig DataFederated Learning
0 likes · 15 min read
JD's Open‑Source Federated Learning Solution 9N‑FL: Architecture, Features, Timeline, and Business Impact
JD Tech
JD Tech
Apr 8, 2021 · Artificial Intelligence

Federated Learning in E‑commerce Marketing: JD.com’s 9N‑FL Platform Overview and Practices

This article explains how data islands hinder AI progress, introduces federated learning as a privacy‑preserving solution, details JD.com’s 9N‑FL platform—including its architecture, features, classification, privacy‑preserving techniques, and algorithm support—and demonstrates its successful application in e‑commerce advertising that yielded over 15% revenue growth.

AIDistributed SystemsFederated Learning
0 likes · 12 min read
Federated Learning in E‑commerce Marketing: JD.com’s 9N‑FL Platform Overview and Practices
JD Cloud Developers
JD Cloud Developers
Feb 10, 2021 · Artificial Intelligence

How JD Tech’s Breakthrough AI Papers Dominated AAAI 2021

JD Tech showcased a remarkable 21-paper presence at AAAI 2021, covering federated learning, spatio‑temporal AI, recommendation systems, computer vision, and causal learning, highlighting the company’s transition from research to real‑world AI applications across smart cities, retail, and risk management.

AAAI 2021Computer VisionFederated Learning
0 likes · 12 min read
How JD Tech’s Breakthrough AI Papers Dominated AAAI 2021
JD Tech Talk
JD Tech Talk
Jan 15, 2021 · Artificial Intelligence

JD Digits Secures 16 Paper Acceptances at AAAI 2021, Showcasing Advances in Federated Learning, Spatio‑Temporal AI and Recommendation Systems

JD Digits announced that 16 of its research papers were accepted at the prestigious AAAI 2021 conference, covering federated learning, vertical federated learning, communication‑efficient SGD, spatio‑temporal graph diffusion for traffic forecasting, robust meta‑learning for sales prediction, graph‑enhanced session recommendation, knowledge‑aware social recommendation, and causal learning for retail delinquency, highlighting the company's strong AI research and its real‑world smart‑city and industry applications.

AAAI2021Federated LearningJD Digits
0 likes · 10 min read
JD Digits Secures 16 Paper Acceptances at AAAI 2021, Showcasing Advances in Federated Learning, Spatio‑Temporal AI and Recommendation Systems
DataFunTalk
DataFunTalk
Dec 18, 2020 · Artificial Intelligence

Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions

This article explains the evolution of federated learning, contrasts Google’s cross‑device horizontal approach with China’s cross‑silo vertical implementations, analyzes their security vulnerabilities, and demonstrates how secure multi‑party computation—including differential privacy, secure aggregation, and secret‑sharing techniques—can address these challenges while highlighting performance trade‑offs.

Federated LearningSecure Aggregationcross-silo
0 likes · 18 min read
Federated Learning and Secure Multi‑Party Computation: Concepts, Security Challenges, and Practical Solutions
JD Tech Talk
JD Tech Talk
Nov 16, 2020 · Artificial Intelligence

Practical Guide to Deploying Federated Learning: Architecture, Deployment, Training, and Inference

This article provides a comprehensive overview of federated learning engineering, covering deployment via Docker containers, the design of training and inference frameworks, key services such as communication, training, model management, and registration, and practical considerations for scaling and reliability in production environments.

AIDeploymentDocker
0 likes · 11 min read
Practical Guide to Deploying Federated Learning: Architecture, Deployment, Training, and Inference
JD Tech Talk
JD Tech Talk
Nov 13, 2020 · Artificial Intelligence

Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference

This article provides a comprehensive engineering overview of federated learning, covering its core distributed‑learning concept, Docker‑based deployment, detailed training‑service architecture with validation, scheduling, metadata, and model‑management components, as well as a complete inference framework and workflow for production use.

AI EngineeringDistributed SystemsDocker
0 likes · 12 min read
Practical Engineering Guide to Federated Learning: Deployment, Training, and Inference
JD Tech Talk
JD Tech Talk
Oct 30, 2020 · Cloud Computing

Federated Learning, Edge Computing, and Cloud Computing: Concepts, Applications, and Comparative Analysis

This article introduces federated learning, edge computing, and cloud computing, explains each technology's principles and use cases, and then compares their similarities and differences, highlighting privacy‑preserving collaborative modeling, near‑source processing, and centralized resource provisioning.

ComparisonEdge ComputingFederated Learning
0 likes · 8 min read
Federated Learning, Edge Computing, and Cloud Computing: Concepts, Applications, and Comparative Analysis
Tencent Cloud Developer
Tencent Cloud Developer
Sep 25, 2020 · Artificial Intelligence

Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption

Tencent Shield‑Federated Computing enables banks to jointly train Gradient Boosted Decision Trees and Logistic Regression with external data owners by using homomorphic encryption to perform encrypted variable and split‑point searches, gradient aggregation, and model updates, delivering near‑centralized accuracy, up to 70 % speed gains, and full data confidentiality for financial risk control.

Federated LearningGradient Boosted TreesHomomorphic Encryption
0 likes · 15 min read
Privacy-Preserving Federated Learning for Financial Risk Control Using Homomorphic Encryption
JD Retail Technology
JD Retail Technology
Sep 15, 2020 · Artificial Intelligence

Federated Learning and the 9NFL Platform: Architecture, Features, and Real‑World Applications

This article explains how federated learning addresses data‑island challenges under privacy regulations, introduces JD.com’s 9NFL federated learning platform, details its component‑based, high‑availability, high‑performance architecture, and showcases a successful advertising use case that boosted revenue by over 10%.

AIFederated LearningJD.com
0 likes · 7 min read
Federated Learning and the 9NFL Platform: Architecture, Features, and Real‑World Applications
AntTech
AntTech
Aug 18, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience

The article compares shared intelligence and federated learning, examines privacy‑preserving techniques such as MPC, TEE, and differential privacy, discusses gradient‑inversion attacks and their mitigations, and presents Ant Group’s end‑to‑end system design and real‑world deployments in finance.

AI securityAnt GroupFederated Learning
0 likes · 22 min read
Shared Intelligence vs. Federated Learning: Techniques, Challenges, and Ant Group’s Practical Experience
JD Tech Talk
JD Tech Talk
Jul 7, 2020 · Artificial Intelligence

Federated Learning vs. Blockchain: Status, Rationale, Comparison, and Complementarity

This article examines the current status, strategic importance, and underlying reasons for federated learning and blockchain, compares their similarities and differences, and explores how their complementary strengths can be combined to create trusted, privacy‑preserving, and value‑transfer solutions in the digital economy.

AIBlockchainDecentralization
0 likes · 10 min read
Federated Learning vs. Blockchain: Status, Rationale, Comparison, and Complementarity
JD Tech Talk
JD Tech Talk
Jul 3, 2020 · Artificial Intelligence

Federated Learning vs Blockchain: Complementary Technologies and Their Integration

This article compares federated learning and blockchain, explains their shared trust foundation, outlines their distinct applications, and describes two integration patterns that combine privacy‑preserving AI with immutable decentralized ledgers to create new value in the digital economy.

BlockchainDecentralizationFederated Learning
0 likes · 10 min read
Federated Learning vs Blockchain: Complementary Technologies and Their Integration
DataFunTalk
DataFunTalk
Jun 4, 2020 · Artificial Intelligence

Exploring Federated Recommendation Algorithms and Their Applications

This article introduces the challenges of traditional centralized recommendation systems, explains the principles and implementations of federated recommendation algorithms—including vertical and horizontal federated matrix factorization and factorization machines—using WeBank’s open-source FATE platform, and discusses cloud services, practical use cases, and performance benefits.

AIFATEFederated Learning
0 likes · 13 min read
Exploring Federated Recommendation Algorithms and Their Applications
JD Tech Talk
JD Tech Talk
Jun 3, 2020 · Artificial Intelligence

JD Digital Science Unveils Fast Secure Federated Learning Framework and Two Industry‑First Techniques

JD Digital Science introduced its fast secure federated learning framework, highlighted two pioneering technologies—a kernel‑based nonlinear federated learning algorithm and a distributed fast homomorphic encryption method—both accepted at KDD 2020, and discussed their industrial applications, privacy benefits, and regulatory relevance.

AI InfrastructureFederated LearningKDD2020
0 likes · 6 min read
JD Digital Science Unveils Fast Secure Federated Learning Framework and Two Industry‑First Techniques
AntTech
AntTech
Jun 2, 2020 · Artificial Intelligence

Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)

The Ant Shared Intelligence workshop at ACM CCS 2020 invites researchers and practitioners to submit short papers on privacy‑preserving machine learning techniques such as secure multi‑party computation, homomorphic encryption, differential privacy, federated learning, and related applications, with a submission deadline of June 21, 2020.

AI securityCCS2020Federated Learning
0 likes · 5 min read
Privacy-Preserving Machine Learning Workshop at CCS 2020 (Ant Shared Intelligence)
Tencent Cloud Developer
Tencent Cloud Developer
Apr 3, 2020 · Cloud Native

TVP Technical Closed-door Conference Summary: Exploring the Dawn of Technology Trends

The TVP Technical Closed‑door Conference gathered 44 experts across eight sessions to examine post‑pandemic technology trends—including blockchain as institutional infrastructure, cloud‑native databases and serverless computing, industrial internet transformation, data‑driven online education, federated learning for privacy‑preserving AI, and the shift toward resource‑focused technical entrepreneurship.

Cloud NativeDigital TransformationFederated Learning
0 likes · 13 min read
TVP Technical Closed-door Conference Summary: Exploring the Dawn of Technology Trends
JD Tech Talk
JD Tech Talk
Apr 3, 2020 · Artificial Intelligence

Federated Learning: Application Prospects, Deployment Challenges, and Implementation Methods

This article examines federated learning’s wide‑range application prospects in healthcare, mobile internet, and finance, analyzes the technical and regulatory challenges of deploying such systems, and explains the concrete implementation steps for horizontal and vertical federated learning architectures.

AIFederated LearningHealthcare
0 likes · 11 min read
Federated Learning: Application Prospects, Deployment Challenges, and Implementation Methods
Tencent Cloud Developer
Tencent Cloud Developer
Mar 29, 2020 · Industry Insights

How Federated Learning Is Breaking Data Silos Across Clouds

This article examines the rise of federated learning as a solution to data islands, detailing regulatory pressures, technical foundations, industry implementations by WeBank, Tencent and VMware, and practical product workflows that enable secure, cross‑cloud AI collaboration.

Big DataData CollaborationFederated Learning
0 likes · 9 min read
How Federated Learning Is Breaking Data Silos Across Clouds
JD Tech Talk
JD Tech Talk
Mar 27, 2020 · Artificial Intelligence

Understanding Federated Learning: Origins, Applications, and Privacy Protection Techniques

This article explains the rapid rise of federated learning, its technical foundations combining machine learning, distributed computing, and privacy protection, practical use cases, intuitive privacy examples, and empirical evidence that it can improve model performance without compromising data security.

Federated Learningartificial intelligencedata security
0 likes · 15 min read
Understanding Federated Learning: Origins, Applications, and Privacy Protection Techniques
AntTech
AntTech
Mar 4, 2020 · Artificial Intelligence

Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance

The article explains how Ant Group tackles the privacy‑usability trade‑off in AI by combining Trusted Execution Environments and Multi‑Party Computation into a “shared intelligence” framework, contrasting it with federated learning, detailing technical architectures, training workflows, and its impact on financial data sharing.

Federated Learningdata sharingfinancial technology
0 likes · 14 min read
Shared Intelligence vs. Federated Learning: Ant Group’s Privacy‑Preserving Machine Learning Solutions for Finance
DataFunTalk
DataFunTalk
Sep 9, 2019 · Artificial Intelligence

Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform

This article presents a comprehensive overview of federated learning, covering its motivation, vertical and horizontal variants, privacy‑preserving technologies, real‑world use cases, and the industrial‑grade open‑source platform FATE that enables secure cross‑organization machine learning.

Data CollaborationFATEFederated Learning
0 likes · 16 min read
Federated Learning: Background, Techniques, Applications, and the FATE Open‑Source Platform