Enhanced Federated Optimization: Adaptive Unbiased Sampling with Reduced Variance

Federated Learning (FL) is a distributed learning paradigm to train a global model across multiple devices without collecting local data. In FL, a server typically selects a subset of clients for each training round to optimize resource usage. Central to this process is the technique of unbiased client sampling, which ensures a representative selection of clients. Current methods primarily utilize a random sampling procedure which, despite its effectiveness, achieves suboptimal efficiency owing to the loose upper bound caused by the …

client cs.lg data devices distributed distributed learning federated learning global multiple optimization paradigm process sampling server train training unbiased usage variance