南京邮电大学学报（自然科学版）2026，Vol.46Issue(2)：84-93,10.DOI:10.14132/j.cnki.1673-5439.2026.02.010
基于深度强化学习的联邦学习客户端自适应选择策略
Adaptive selection for clients in federated learning based on deep reinforcement learning
摘要
Abstract
Federated learning(FL)is a distributed paradigm for addressing data isolation by enabling global model training without clients sharing raw data,thereby protecting user privacy.Due to the large number of clients and limited communication resources,only a subset of clients can participate in model aggregation.Nonetheless,FL systems still face challenges such as device heterogeneity and data hetero-geneity.Naive client selection strategies fail to adapt to environmental dynamics,resulting in slow model convergence and degraded performance.To address these,this paper proposes a novel client availability metric by considering time-varying client states,and formulates a multi-constrained client selection model as a loss minimization problem.Then,this problem is further modeled as a Markov decision pro-cess,and a deep reinforcement learning-based adaptive client selection algorithm(ASC-DRL)is de-signed.ASC-DRL comprehensively optimizes communication latency,resource consumption,and client availability through continuous agent-environment interactions,maximizing a reward function,to obtain the optimal client selection scheme.Experiment results demonstrate that ASC-DRL improves model accu-racy by 89.2%and reduces training loss by 99.8%compared to traditional methods,while adaptively en-hancing the performance and stability of FL in dynamic environments.关键词
联邦学习/深度强化学习/客户端选择/自适应选择Key words
federated learning(FL)/deep reinforcement learning/client selection/adaptive selection分类
信息技术与安全科学引用本文复制引用
孙洪波,王国成,张林,王晔,郭永安..基于深度强化学习的联邦学习客户端自适应选择策略[J].南京邮电大学学报（自然科学版）,2026,46(2):84-93,10.基金项目
江苏省前沿引领技术基础研究专项(BK20202001)和江苏省创新支撑计划政府间双边创新合作项目(BZ2023018)资助项目 （BK20202001）
