{"type":"ScholarlyArticle","title":"FedDWA: Personalized Federated Learning with Dynamic Weight Adjustment","authors":[{"name":"Jiahao Liu","slug":"jiahao-liu","given":"Jiahao","family":"Liu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Jiang Wu","slug":"jiang-wu","given":"Jiang","family":"Wu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Jinyu Chen","slug":"jinyu-chen","given":"Jinyu","family":"Chen","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Miao Hu","slug":"miao-hu","given":"Miao","family":"Hu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"},{"name":"Yipeng Zhou","slug":"yipeng-zhou","given":"Yipeng","family":"Zhou","affiliation":"School of Computing , Faculty of Science and Engineering , Macquarie University , Sydney , Australia"},{"name":"Di Wu","slug":"di-wu","given":"Di","family":"Wu","affiliation":"School of Computer Science and Engineering , Sun Yat-sen University , Guangzhou , China; Guangdong Key Laboratory of Big Data Analysis and Processing , Guangzhou , China"}],"abstract":"Different from conventional federated learning, personalized federated learning (PFL) is able to train a customized model for each individual client according to its unique requirement. The mainstream approach is to adopt a kind of weighted aggregation method to generate personalized models, in which weights are determined by the loss value or model parameters among different clients. However, such kinds of methods require clients to download others' models. It not only sheer increases communication traffic but also potentially infringes data privacy. In this paper, we propose a new PFL algorithm called FedDWA (Federated Learning with Dynamic Weight Adjustment) to address the above problem, which leverages the parameter server (PS) to compute personalized aggregation weights based on collected models from clients. In this way, FedDWA can capture similarities between clients with much less communication overhead. More specifically, we formulate the PFL problem as an optimization problem by minimizing the distance between personalized models and  guidance models, so as to  customize aggregation weights for each client. Guidance models are obtained by  the local one-step ahead adaptation on individual clients. Finally,  we conduct extensive experiments using five real datasets and the results demonstrate that FedDWA can significantly reduce the communication traffic and achieve much higher model accuracy than the state-of-the-art approaches.","doi":"10.24963/ijcai.2023/444","url":"https://ijcai.org/proceedings/2023/444","pdf":"https://ijcai.org/proceedings/2023/0444.pdf","markdown":"https://ijcai.org/proceedings/2023/444.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/444","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":444,"pages":{"first":3993,"last":4001},"keywords":["Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Acar et al., 2021] Durmus Alp Emre Acar, Yue Zhao, Ruizhao Zhu, Ramon Matas, Matthew Mattina, Paul Whatmough, and Venkatesh Saligrama. 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