A Survey on Blockchain-Based Federated Learning:Categorization,Application and Analysis  

作　　者：Yuming Tang Yitian Zhang Tao Niu Zhen Li Zijian Zhang Huaping Chen Long Zhang 

机构地区：[1]School of Cyberspace Science&Technology,Beijing Institute of Technology,Beijing,100081,China [2]School of Computer Science&Technology,Beijing Institute of Technology,Beijing,100081,China [3]Southeast Institute of Information Technology,Beijing Institute of Technology,Putian,351100,China [4]Qianxin Technology Group Co.,Ltd.,Beijing,100044,China

出　　处：《Computer Modeling in Engineering & Sciences》2024年第6期2451-2477,共27页工程与科学中的计算机建模（英文）

基　　金：supported by High-performance Reliable Multi-Party Secure Computing Technology and Product Project for Industrial Internet No.TC220H056.

摘　　要：Federated Learning(FL),as an emergent paradigm in privacy-preserving machine learning,has garnered significant interest from scholars and engineers across both academic and industrial spheres.Despite its innovative approach to model training across distributed networks,FL has its vulnerabilities;the centralized server-client architecture introduces risks of single-point failures.Moreover,the integrity of the global model—a cornerstone of FL—is susceptible to compromise through poisoning attacks by malicious actors.Such attacks and the potential for privacy leakage via inference starkly undermine FL’s foundational privacy and security goals.For these reasons,some participants unwilling use their private data to train a model,which is a bottleneck in the development and industrialization of federated learning.Blockchain technology,characterized by its decentralized ledger system,offers a compelling solution to these issues.It inherently prevents single-point failures and,through its incentive mechanisms,motivates participants to contribute computing power.Thus,blockchain-based FL(BCFL)emerges as a natural progression to address FL’s challenges.This study begins with concise introductions to federated learning and blockchain technologies,followed by a formal analysis of the specific problems that FL encounters.It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL.In addition,this research examines the use of BCFL in various fields,such as the Internet of Things and the Internet of Vehicles.Finally,it assesses the effectiveness of these solutions.

关 键 词：Federated learning blockchain PRIVACY-PRESERVING 

分 类 号：TP31[自动化与计算机技术—计算机软件与理论]