测试技术学报2025，Vol.39Issue(1)：46-53,8.DOI:10.62756/csjs.1671-7449.2025008
不可靠通信下基于信誉的联邦学习客户端选择
Reputation-Based Client Selection for Federated Learning Under Unreliable Communication
贾惠景 1付芳 2张志才2
作者信息
- 1. 山西大学 物理电子工程学院,山西 太原 030051
- 2. 海南大学 计算机科学与技术学院,海南 海口 570228
- 折叠
摘要
Abstract
Federated learning is a distributed machine learning framework that has received widespread attention for its data protection properties.However,malicious clients and unreliable communication seri-ously affect its performance and efficiency.To solve the above problems,a reputation-based federated learning client selection mechanism for multi-task publishers in unreliable communication is proposed.Firstly,the communication reliability is evaluated using the uplink model transmission success probability and its impact on the performance of the aggregation model is also considered.Secondly,a comprehensive client reputation evaluation method is proposed and a client selection mechanism with the optimization objective of maximizing the performance of the aggregation model of the task publisher as well as the reputation-price ratio of the selected client is constructed.To solve this optimization problem,it is mod-eled as a Markov decision process and the curiosity driven deep Q-learning network algorithm is used to achieve optimization.The result shows that the proposed algorithm outperforms the baselines,leading to a significant improvement in the performance of federated learning.关键词
联邦学习/不可靠通信/信誉/客户端选择/好奇心驱动的深度Q学习Key words
federated learning/unreliable communication/reputation/client selection/curiosity driven deep Q-learning network(CDQN)分类
计算机与自动化引用本文复制引用
贾惠景,付芳,张志才..不可靠通信下基于信誉的联邦学习客户端选择[J].测试技术学报,2025,39(1):46-53,8.
