FedCLCC:A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing

作     者：Kangning Yin Xinhui Ji Yan Wang Zhiguo Wang Kangning Yin;Xinhui Ji;Yan Wang;Zhiguo Wang

作者机构：School of Information and Software EngineeringUniversity of Electronic Science and Technology of ChinaChengdu611731China Institute of Public SecurityKash Institute of Electronics and Information IndustryKashi844000China 

出 版 物：《Defence Technology(防务技术)》 (Defence Technology)

年 卷 期：2025年第43卷第1期

页      面：80-93页

核心收录：

学科分类：070801[理学-固体地球物理学] 07[理学] 08[工学] 0708[理学-地球物理学] 0816[工学-测绘科学与技术] 

基　　金：supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187) 

主　　题：Federated learning Statistical heterogeneity Personalized model Conditional computing Contrastive learning 

摘      要：Federated learning(FL)is a distributed machine learning paradigm for edge cloud *** can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge ***,the diversity of clients in edge cloud computing presents significant challenges for *** federated learning(pFL)received considerable attention in recent *** example of pFL involves exploiting the global and local information in the local *** pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized *** achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional *** core of FedCLCC is the use of contrastive learning and conditional *** learning determines the feature representation similarity to adjust the local *** computing separates the global and local information and feeds it to their corresponding heads for global and local *** comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.