{"type":"ScholarlyArticle","title":"Dirichlet-based Uncertainty Quantification for Personalized Federated Learning with Improved Posterior Networks","authors":[{"name":"Nikita Kotelevskii","slug":"nikita-kotelevskii","given":"Nikita","family":"Kotelevskii","affiliation":"Skolkovo Institute of Science and Technology (Skoltech) , Moscow , Russia; Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Samuel Horváth","slug":"samuel-horvath","given":"Samuel","family":"Horváth","affiliation":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Karthik Nandakumar","slug":"karthik-nandakumar","given":"Karthik","family":"Nandakumar","affiliation":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Martin Takac","slug":"martin-takac","given":"Martin","family":"Takac","affiliation":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"},{"name":"Maxim Panov","slug":"maxim-panov","given":"Maxim","family":"Panov","affiliation":"Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) , Abu Dhabi , UAE"}],"abstract":"In modern federated learning, one of the main challenges is to account for inherent heterogeneity and the diverse nature of data distributions for different clients. This problem is often addressed by introducing personalization of the models towards the data distribution of the particular client. However, a personalized model might be unreliable when applied to the data that is not typical for this client. Eventually, it may perform worse for these data than the non-personalized global model trained in a federated way on the data from all the clients. This paper presents a new approach to federated learning that allows selecting a model from global and personalized ones that would perform better for a particular input point. It is achieved through a careful modeling of predictive uncertainties that helps to detect local and global in- and out-of-distribution data and use this information to select the model that is confident in a prediction. The comprehensive experimental evaluation on the popular real-world image datasets shows the superior performance of the model in the presence of out-of-distribution data while performing on par with state-of-the-art personalized federated learning algorithms in the standard scenarios.","doi":"10.24963/ijcai.2024/788","url":"https://ijcai.org/proceedings/2024/788","pdf":"https://ijcai.org/proceedings/2024/0788.pdf","markdown":"https://ijcai.org/proceedings/2024/788.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/788","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":"Uncertainty in AI","track":"Main Track","number":788,"pages":{"first":7127,"last":7135},"keywords":["Machine Learning: ML: Federated learning","Uncertainty in AI: UAI: Bayesian networks","Machine Learning: ML: Bayesian learning","Machine Learning: ML: Probabilistic machine learning"],"videos":[],"references":[{"position":1,"raw":"[Arivazhagan et al., 2019] Manoj Ghuhan Arivazhagan, Vinay Aggarwal, Aaditya Kumar Singh, and Sunav Choudhary. 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