A Federated Learning Framework with Blockchain-Based Auditable Participant Selection  

作　　者：Huang Zeng Mingtian Zhang Tengfei Liu Anjia Yang 

机构地区：[1]College of Cyber Security,Jinan University,Guangzhou,510632,China

出　　处：《Computers, Materials & Continua》2024年第6期5125-5142,共18页计算机、材料和连续体（英文）

基　　金：supported by the Key-Area Research and Development Program of Guangdong Province under Grant No.2020B0101090004;the National Natural Science Foundation of China under Grant No.62072215,the Guangzhou Basic Research Plan City-School Joint Funding Project under Grant No.2024A03J0405;the Guangzhou Basic and Applied Basic Research Foundation under Grant No.2024A04J3458;the State Archives Administration Science and Technology Program Plan of China under Grant 2023-X-028.

摘　　要：Federated learning is an important distributed model training technique in Internet of Things(IoT),in which participant selection is a key component that plays a role in improving training efficiency and model accuracy.This module enables a central server to select a subset of participants to performmodel training based on data and device information.By doing so,selected participants are rewarded and actively perform model training,while participants that are detrimental to training efficiency and model accuracy are excluded.However,in practice,participants may suspect that the central server may have miscalculated and thus not made the selection honestly.This lack of trustworthiness problem,which can demotivate participants,has received little attention.Another problem that has received little attention is the leakage of participants’private information during the selection process.We will therefore propose a federated learning framework with auditable participant selection.It supports smart contracts in selecting a set of suitable participants based on their training loss without compromising the privacy.Considering the possibility of malicious campaigning and impersonation of participants,the framework employs commitment schemes and zero-knowledge proofs to counteract these malicious behaviors.Finally,we analyze the security of the framework and conduct a series of experiments to demonstrate that the framework can effectively improve the efficiency of federated learning.

关 键 词：Federated learning internet of things participant selection blockchain auditability PRIVACY 

分 类 号：TP311[自动化与计算机技术—计算机软件与理论]