AMFL: Asynchronous Multi-level Federated Learning with Client Selection
Xuerui Li, Yangming Zhao, Chunming Qiao · 2024
Synchronous Federated Learning (FL) may suffer from increased training time and costs. To address this issue, Asynchronous Federated Learning (AFL) has been proposed. Furthermore, traditional single-level FL with just one cloud server and multiple clients may incur long communication delays, due to the absence of intermediate nodes. As a solution, Hierarchical FL (HFL) and Multi-level FL have been proposed to overcome this limitation. In this work, we integrate Asynchronous FL and Multi-level FL, by employing a creative Client Selection method to avoid the accumulation of outdated updates during multi-level aggregation, called Asynchronous Multi-level Federated Learning with Client Selection (AMFL) method. We evaluate AMFL’s performance with that of Asynchronous FL, Hierarchical FL, Multi-level FL, and other stateof-the-art baselines. The results indicate that AMFL converges faster than these methods.