Using the previous posterior as the next prior in federated SGLD training cut iterations to 85% accuracy by about 50% over three days of radar data, with improved calibration.
Federated Generalized Bayesian Learning via Distributed Stein Variational Gradient Descent
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abstract
This paper introduces Distributed Stein Variational Gradient Descent (DSVGD), a non-parametric generalized Bayesian inference framework for federated learning. DSVGD maintains a number of non-random and interacting particles at a central server to represent the current iterate of the model global posterior. The particles are iteratively downloaded and updated by one of the agents with the end goal of minimizing the global free energy. By varying the number of particles, DSVGD enables a flexible trade-off between per-iteration communication load and number of communication rounds. DSVGD is shown to compare favorably to benchmark frequentist and Bayesian federated learning strategies, also scheduling a single device per iteration, in terms of accuracy and scalability with respect to the number of agents, while also providing well-calibrated, and hence trustworthy, predictions.
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cs.LG 1years
2025 1verdicts
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Bayesian Federated Learning for Continual Training
Using the previous posterior as the next prior in federated SGLD training cut iterations to 85% accuracy by about 50% over three days of radar data, with improved calibration.