Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning

• URL: http://arxiv.org/abs/2509.10132v1
• Date: Fri, 12 Sep 2025 10:46:21 GMT
• Title: Cost-Free Personalization via Information-Geometric Projection in Bayesian Federated Learning
• Authors: Nour Jamoussi, Giuseppe Serra, Photios A. Stavrou, Marios Kountouris,
• Abstract summary: Federated Learning (BFL) combines uncertainty modeling with decentralized training.<n>We propose an information-geometric projection framework for personalization in BFL.
• Score: 8.959959863080888
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bayesian Federated Learning (BFL) combines uncertainty modeling with decentralized training, enabling the development of personalized and reliable models under data heterogeneity and privacy constraints. Existing approaches typically rely on Markov Chain Monte Carlo (MCMC) sampling or variational inference, often incorporating personalization mechanisms to better adapt to local data distributions. In this work, we propose an information-geometric projection framework for personalization in parametric BFL. By projecting the global model onto a neighborhood of the user's local model, our method enables a tunable trade-off between global generalization and local specialization. Under mild assumptions, we show that this projection step is equivalent to computing a barycenter on the statistical manifold, allowing us to derive closed-form solutions and achieve cost-free personalization. We apply the proposed approach to a variational learning setup using the Improved Variational Online Newton (IVON) optimizer and extend its application to general aggregation schemes in BFL. Empirical evaluations under heterogeneous data distributions confirm that our method effectively balances global and local performance with minimal computational overhead.

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