{"type":"ScholarlyArticle","title":"Bayesian Federated Learning: A Survey","authors":[{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"University of Technology Sydney , Australia; Macquarie University , Australia"},{"name":"Hui Chen","slug":"hui-chen","given":"Hui","family":"Chen","affiliation":"University of Technology Sydney , Australia"},{"name":"Xuhui Fan","slug":"xuhui-fan","given":"Xuhui","family":"Fan","affiliation":"University of New Castle , Australia"},{"name":"Joao Gama","slug":"joao-gama","given":"Joao","family":"Gama","affiliation":"INESC TEC -LIAAD , Portugal"},{"name":"Yew-Soon Ong","slug":"yew-soon-ong","given":"Yew-Soon","family":"Ong","affiliation":"Nanyang Technological University , Singapore"},{"name":"Vipin Kumar","slug":"vipin-kumar","given":"Vipin","family":"Kumar","affiliation":"University of Minnesota , USA"}],"abstract":"Federated learning (FL) demonstrates its advantages in integrating distributed infrastructure, communication, computing and learning in a privacy-preserving manner. However, the robustness and capabilities of existing FL methods are challenged by limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability. Bayesian federated learning (BFL) has emerged as a promising approach to address these issues. This survey presents a critical overview of BFL, including its basic concepts, its relations to Bayesian learning in the context of FL, and a taxonomy of BFL from both Bayesian and federated perspectives. We categorize and discuss client- and server-side and FL-based BFL methods and their pros and cons. The limitations of the existing BFL methods and the future directions of BFL research further address the intricate requirements of real-life FL applications.","doi":"10.24963/ijcai.2023/851","url":"https://ijcai.org/proceedings/2023/851","pdf":"https://ijcai.org/proceedings/2023/0851.pdf","markdown":"https://ijcai.org/proceedings/2023/851.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/851","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":"Late Papers","track":"Survey Track","number":851,"pages":{"first":7233,"last":7242},"keywords":["Survey: Data Mining","Survey: Machine Learning","Survey: Uncertainty in AI","Survey: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Abrial, 2009] Jean-Raymond Abrial. 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