Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework

• URL: http://arxiv.org/abs/2502.00846v1
• Date: Sun, 02 Feb 2025 16:39:37 GMT
• Title: Federated Generalised Variational Inference: A Robust Probabilistic Federated Learning Framework
• Authors: Terje Mildner, Oliver Hamelijnck, Paris Giampouras, Theodoros Damoulas,
• Abstract summary: FedGVI is a probabilistic Federated Learning (FL) framework that is provably robust to both prior and likelihood misspecification. We offer theoretical analysis in terms of fixed-point convergence, optimality of the cavity distribution, and provable robustness.
• Score: 12.454538785810259
• License:
• Abstract: We introduce FedGVI, a probabilistic Federated Learning (FL) framework that is provably robust to both prior and likelihood misspecification. FedGVI addresses limitations in both frequentist and Bayesian FL by providing unbiased predictions under model misspecification, with calibrated uncertainty quantification. Our approach generalises previous FL approaches, specifically Partitioned Variational Inference (Ashman et al., 2022), by allowing robust and conjugate updates, decreasing computational complexity at the clients. We offer theoretical analysis in terms of fixed-point convergence, optimality of the cavity distribution, and provable robustness. Additionally, we empirically demonstrate the effectiveness of FedGVI in terms of improved robustness and predictive performance on multiple synthetic and real world classification data sets.

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