{"type":"ScholarlyArticle","title":"FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimization","authors":[{"name":"Zhiyuan Ning","slug":"zhiyuan-ning","given":"Zhiyuan","family":"Ning","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Chunlin Tian","slug":"chunlin-tian","given":"Chunlin","family":"Tian","affiliation":"Department of Computer and Information Science , IOTSC , University of Macau"},{"name":"Meng Xiao","slug":"meng-xiao","given":"Meng","family":"Xiao","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Wei Fan","slug":"wei-fan","given":"Wei","family":"Fan","affiliation":"University of Oxford"},{"name":"Pengyang Wang","slug":"pengyang-wang","given":"Pengyang","family":"Wang","affiliation":"Department of Computer and Information Science , IOTSC , University of Macau"},{"name":"Li Li","slug":"li-li","given":"Li","family":"Li","affiliation":"Department of Computer and Information Science , IOTSC , University of Macau"},{"name":"Pengfei Wang","slug":"pengfei-wang","given":"Pengfei","family":"Wang","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences"},{"name":"Yuanchun Zhou","slug":"yuanchun-zhou","given":"Yuanchun","family":"Zhou","affiliation":"Computer Network Information Center , Chinese Academy of Sciences; University of Chinese Academy of Sciences; Hangzhou Institute for Advanced Study , UCAS"}],"abstract":"Federated Learning faces significant challenges in statistical and system heterogeneity, along with high energy consumption, necessitating efficient client selection strategies. Traditional approaches, including heuristic and learning-based methods, fall short of addressing these complexities holistically. In response, we propose FedGCS, a novel generative client selection framework that innovatively recasts the client selection process as a generative task. Drawing inspiration from the methodologies used in large language models, FedGCS efficiently encodes abundant decision-making knowledge within a continuous representation space, enabling efficient gradient-based optimization to search for optimal client selection that will be finally output via generation. The framework comprises four steps: (1) automatic collection of diverse “selection-score” pair data using classical client selection methods; (2) training an encoder-evaluator-decoder framework on this data to construct a continuous representation space; (3) employing gradient-based optimization in this space for optimal client selection; (4) generating the final optimal client selection via using beam search for the well-trained decoder. FedGCS outperforms traditional methods by being more comprehensive, generalizable, and efficient, simultaneously optimizing for model performance, latency, and energy consumption. The effectiveness of FedGCS is proven through extensive experimental analyses.","doi":"10.24963/ijcai.2024/526","url":"https://ijcai.org/proceedings/2024/526","pdf":"https://ijcai.org/proceedings/2024/0526.pdf","markdown":"https://ijcai.org/proceedings/2024/526.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/526","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":526,"pages":{"first":4760,"last":4768},"keywords":["Machine Learning: ML: Applications","Data Mining: DM: Applications","Data Mining: DM: Other","Machine Learning: ML: Federated learning"],"videos":[],"references":[{"position":1,"raw":"[Bonawitz et al., 2019] Keith Bonawitz, Hubert Eichner, Wolfgang Grieskamp, Dzmitry Huba, Alex Ingerman, Vladimir Ivanov, Chloe Kiddon, Jakub Konečnỳ, Stefano Mazzocchi, Brendan McMahan, et al. 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