Computation-Effective Personalized Federated Learning: A Meta Learning Approach
Ziwei Zhan, Xiaoxi Zhang · 2023
Federated learning has gained widespread attention because of its protection of data privacy. It faces two key challenges, one is network bottleneck and stragglers due to performance differences among clients, and the other is performance degradation due to data heterogeneity. Per-FedAvg is a variant of FedAvg that utilizes model-agnostic meta-learning to achieve personalization. However, Per-FedAvg is computationally demanding, which can potentially cause severe straggler effects. In this work, we propose a strategy which allows resource-constrained clients to use the local update of FedAvg as an approximation to the local update of Per-FedAvg. Theoretical results show that the same convergence rate can be achieved when a fraction of the clients use the local update of FedAvg as the approximate update.