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Conventional frequentist FL schemes are known to yield overconfidentdecisions. Bayesian FL addresses this issue by allowing agents to process andexchange uncertainty information encoded in distributions over the modelparameters. However, this comes at the cost of a larger per-iterationcommunication overhead. This letter investigates whether Bayesian FL can stillprovide advantages in terms of calibration when constraining communicationbandwidth. We present compressed particle-based Bayesian FL protocols for FLand federated "unlearning" that apply quantization and sparsification acrossmultiple particles. The experimental results confirm that the benefits ofBayesian FL are robust to bandwidth constraints.

Compressed Particle-Based Federated Bayesian Learning and Unlearning