论文标题
压缩粒子的联合贝叶斯学习和学习
Compressed Particle-Based Federated Bayesian Learning and Unlearning
论文作者
论文摘要
已知常规的频繁FL计划会产生过度自信的决策。贝叶斯FL通过允许代理商在分布中对模型参数编码的不确定性信息进行处理和交换不确定性信息来解决此问题。但是，这是以较大的触电通信开销为代价的。这封信调查了贝叶斯佛罗里达州在限制通信带宽时是否仍然可以在校准方面提供优势。我们提出了针对FL的压缩粒子贝叶斯FL方案，并为跨多个颗粒应用量化和稀疏而联合的“学习”。实验结果证实，贝叶斯FL的益处对带宽约束是可靠的。
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.
