{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/federated-generalised-variational-inference-a","title":"Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework","arxiv_id":"2502.00846","date":"2025-02-02","proceeding":null,"authors":["Terje Mildner","Oliver Hamelijnck","Paris Giampouras","Theodoros Damoulas"],"abstract":"We introduce FedGVI, a probabilistic Federated Learning (FL) framework that is robust to both prior and likelihood misspecification. 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