The article analyzes how emerging data‑sovereignty regulations worldwide force enterprises to rebuild cloud architectures, adopt verifiable key control, zero‑trust data access, and policy‑as‑code, while offering practical roadmaps and pitfalls for securing data in 2026.
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.
Xiaomi announced that a suite of AI research papers—including a large‑scale audio‑text dataset, a federated learning framework for domain and class generalization, a dual‑encoder music evaluation model, a cross‑domain audio‑text pre‑training system, a one‑step video‑to‑audio synthesis method, a training‑free frame‑selection technique for long‑video understanding, and a unified multimodal retrieval architecture—were accepted to the prestigious ICASSP 2026 conference, showcasing detailed methodologies, benchmark results, and potential impact across audio, vision, and multimodal AI applications.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
A team of university students built an AI‑powered eye‑disease diagnostic system using federated learning and cloud services, achieving 97.48% accuracy and winning a national award, while outlining future plans for 3D visualization and immersive medical interaction.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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%.
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.
This article announces a live broadcast on federated learning for advertising and recommendation, featuring experts from Tencent Advertising and WeBank, discussing how federated learning enables privacy-preserving data sharing to enhance marketing effectiveness.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
