文献类型：期刊文献

英文题名：pFL-SBPM: A communication-efficient personalized federated learning framework for resource-limited edge clients

作者：Hu, Han[1,2];Du, Wenli[1,2,3];Li, Yuqiang[1,2];Wang, Yue[1,2]

机构：[1]East China Univ Sci & Technol, State Key Lab Ind Control Technol, Shanghai 200237, Peoples R China;[2]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[3]Huzhou Inst Ind Control Technol, Huzhou 313099, Peoples R China

年份：2025

卷号：171

外文期刊名：FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE

收录：;EI(收录号：20251718276761);WOS:【SCI-EXPANDED(收录号:WOS:001478703200001)】；

基金：This work was supported by the National Key Research and Development Program of China (2022YFB3305900) , National Natural Science Foundation of China (Key Program: 62136003) , the State Key Laboratory of Industrial Control Technology, China (Grant No. ICT2024A24) and Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Personalized federated learning; Communication efficiency; Mask optimization; Randomly weighted network

摘要：Federated learning has attracted widespread attention due to its privacy-preserving characteristic. However, in real-world scenarios, the heterogeneity of decentralized data and the limited communication resources of clients pose great challenges to the deployment of federated training. Although existing works have made great strides in dealing with heterogeneous data or compressing communication, they struggle to strike a balance between model accuracy and communication cost. To address the above issues, this paper proposes a novel federated learning framework called pFL-SBPM, which achieves communication-efficient personalized Federated Learning through Stochastic Binary Probability Masks. Specifically, we utilize probability mask optimization instead of conventional weight training, where clients obtain personalized sparse subnetworks adapted to local task requirements by cooperative optimization of probability masks in a randomly weighted network. We develop an uplink communication strategy based on stochastic binary masks and a downlink communication strategy based on binary encoding and decoding, which achieves enhanced privacy protection while dramatically reducing the communication cost. Furthermore, to effectively handle heterogeneous data while mitigating the negative impact of the introduction of stochasticity on the stability of federated training, we carefully design a soft-threshold based selective updating strategy for probability masks. The experimental results show the significant superiority and competitiveness of pFL-SBPM compared to existing baseline and state-of-the-art methods in terms of inference accuracy, communication cost, computational cost and model size.