Conventional frequentist FL schemes are known to yield overconfident decisions. Bayesian FL addresses this issue by allowing agents to process and exchange uncertainty information encoded in distributions over the model parameters. However, this comes at the cost of a larger per-iteration communication overhead. This letter investigates whether Bayesian FL can still provide advantages in terms of calibration when constraining communication bandwidth. We present compressed particle-based Bayesian FL protocols for FL and federated "unlearning" that apply quantization and sparsification across multiple particles. The experimental results confirm that the benefits of Bayesian FL are robust to bandwidth constraints.

翻译：据知,常规常客FL计划会做出过于自信的决定。Bayesian FL允许代理商处理和交换模型参数分布中编码的不确定性信息。然而,这需要花费更大的每度通信管理费。本信调查Bayesian FL在限制通信带宽时是否仍能在校准方面提供优势。我们为FL提出了基于压缩粒子的Bayesian FL协议,并结合了“不学习”协议,对多个粒子进行了定量和透析。实验结果证实Bayesian FL的好处对于带宽限制是强大的。