{"title":"FedCLCC: A personalized federated learning algorithm for edge cloud collaboration based on contrastive learning and conditional computing","authors":"Kangning Yin, Xinhui Ji, Yan Wang, Zhiguo Wang","doi":"10.1016/j.dt.2024.08.015","DOIUrl":null,"url":null,"abstract":"Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. However, the diversity of clients in edge cloud computing presents significant challenges for FL. Personalized federated learning (pFL) received considerable attention in recent years. One example of pFL involves exploiting the global and local information in the local model. Current 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 learning. To achieve high pFL performance, we propose FedCLCC: Federated Contrastive Learning and Conditional Computing. The core of FedCLCC is the use of contrastive learning and conditional computing. Contrastive learning determines the feature representation similarity to adjust the local model. Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling. Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.","PeriodicalId":10986,"journal":{"name":"Defence Technology","volume":null,"pages":null},"PeriodicalIF":5.1000,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Defence Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1016/j.dt.2024.08.015","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
Federated learning (FL) is a distributed machine learning paradigm for edge cloud computing. FL can facilitate data-driven decision-making in tactical scenarios, effectively addressing both data volume and infrastructure challenges in edge environments. However, the diversity of clients in edge cloud computing presents significant challenges for FL. Personalized federated learning (pFL) received considerable attention in recent years. One example of pFL involves exploiting the global and local information in the local model. Current 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 learning. To achieve high pFL performance, we propose FedCLCC: Federated Contrastive Learning and Conditional Computing. The core of FedCLCC is the use of contrastive learning and conditional computing. Contrastive learning determines the feature representation similarity to adjust the local model. Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling. Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
期刊介绍:
Defence Technology, sponsored by China Ordnance Society, is published quarterly and aims to become one of the well-known comprehensive journals in the world, which reports on the breakthroughs in defence technology by building up an international academic exchange platform for the defence technology related research. It publishes original research papers having direct bearing on defence, with a balanced coverage on analytical, experimental, numerical simulation and applied investigations. It covers various disciplines of science, technology and engineering.