联邦学习中隐私保护聚合机制综述

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Survey of privacy-preserving aggregation mechanisms in federated learning

Qiu Jiana，Ma Haiyinga†，Wang Zhanjun ，Shen Jinyua （a.SchoolofclecofUi China）

Abstract：Asanewdistributed machine learning（DML）framework，FLcanefectively protectthelocaldata privacyof participantsbyaggregatingthelocalmodelparametersuploadedbyparticipantstotraintheglobalmodel.However，theselocal model parameters still have the risk of revealing the privacy of participants. As a critical step in FL ， the privacy-preserving aggregation （ PPAgg ）mechanism has become a key technology for addressing privacy issues.This paper first introduced the concept of FL and its associated privacyand security threats.It then highlighted the core ideas and key proceduresof PPAgg mechanisms by integrating existing privacy-preserving techniques inFL.This paper analyzed typical PPAgg mechanisms indetail，focsingontheirprimaryadvantagesandlimitations，aswellasthespecificapplicationscenarioswhereheyweresuitable.Finall，this papersummarized andanalyzed curent PPAgg mechanisms，explored emerging challenges anddevelopment directions for FL ，and proposed potential solutions to address these issues.

Key words：federated learning（FL）；privacy-preserving；aggregation mechanism；blockchain；securemulti-partycomputation

0 引言

机器学习（ML）作为实现人工智能的一种重要手段，利用收集的原始数据训练特定场景下的数据模型，以达到使机器模拟人类行为的目标。（剩余30283字）