Federated Learning

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.

AIData SecurityFederated Learning

0 likes · 8 min read

SecretFlow Meetup Chengdu – Privacy Computing Innovation and Application Practice

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.

Federated Learningcontinual learningdeep learning

0 likes · 7 min read

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

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.

Data SecurityFLAIRFederated Learning

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

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

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.

Artificial IntelligenceBig DataFederated Learning

0 likes · 13 min read

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

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.

Advertising AnalyticsData SecurityFederated Learning

0 likes · 13 min read

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

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.

Artificial IntelligenceData IslandFederated Learning

0 likes · 19 min read

Federated Learning: Privacy-Preserving Collaborative AI Across Data Islands

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

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.

AIBig DataData Security

0 likes · 14 min read

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

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

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

Alimama Tech

Mar 8, 2023 · Artificial Intelligence

Secure Data Hub: Alibaba's Marketing Privacy Computing Platform

Alibaba’s Secure Data Hub (SDH) is a privacy‑preserving data clean‑room platform that uses secure multi‑party computation and privacy‑enhancing machine learning to let advertisers, ad platforms, and auditors jointly analyze marketing data via a simple SQL API while keeping raw data encrypted, column‑level protected, and confined to each party’s private domain.

Big DataData Clean RoomFederated Learning

0 likes · 13 min read

Secure Data Hub: Alibaba's Marketing Privacy Computing Platform

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 GroupData SecurityFederated Learning

0 likes · 8 min read

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

DataFunSummit

Jan 26, 2023 · Artificial Intelligence

Practical Experience of Federated Learning in Urban Applications

This article shares practical experiences of applying federated learning and privacy computing in urban scenarios, covering fundamental concepts, system architecture, data flow, security measures, real‑world city case studies, current challenges, and future outlook for AI‑driven smart city solutions.

Artificial IntelligenceData SecurityFederated Learning

0 likes · 9 min read

Practical Experience of Federated Learning in Urban Applications

DataFunSummit

Jan 3, 2023 · Artificial Intelligence

Federated Learning Technology Application Innovation Exploration

This presentation reviews the rapid rise of privacy‑preserving computation and federated learning since 2018, explains the fundamentals and classifications of federated learning, and details five technical innovations implemented by China Telecom—including a standard architecture, data‑pollution detection, anti‑member‑inference inference, asynchronous optimization, and contribution‑value assessment—demonstrating practical AI solutions for large‑scale data security and privacy.

Artificial IntelligenceData SecurityFederated Learning

0 likes · 18 min read

Federated Learning Technology Application Innovation Exploration

Alimama Tech

Jan 3, 2023 · Information Security

Alibaba Mama Secure Data Hub Passes Trusted Privacy Computing Evaluation

Alibaba Mama’s Secure Data Hub, a privacy‑preserving data clean‑room platform that uses multi‑party computation and federated learning for secure advertising analytics, successfully passed the China Academy of Information and Communications Technology’s Trusted Privacy Computing evaluation, confirming its strong security, compliance and industry‑recognized capabilities.

AlibabaData Clean RoomFederated Learning

0 likes · 3 min read

Alibaba Mama Secure Data Hub Passes Trusted Privacy Computing Evaluation

DataFunSummit

Dec 28, 2022 · Artificial Intelligence

Federated Learning in Advertising: Business Background, Conversion Flow, Algorithmic Techniques, Vertical & Horizontal FL, and Security

This article explains how federated learning is applied to the advertising industry, covering business background, conversion processes from user, client, and server perspectives, algorithmic components such as CTR and CVR models, vertical and horizontal federated learning architectures, compression techniques, and security challenges with corresponding defenses.

Conversion TrackingFederated LearningHorizontal FL

0 likes · 22 min read

Federated Learning in Advertising: Business Background, Conversion Flow, Algorithmic Techniques, Vertical & Horizontal FL, and Security

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

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.

Data SecurityFederated Learningdigital government

0 likes · 18 min read

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

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.

Federated Learningad rankingbudget optimization

0 likes · 29 min read

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