F2MKD: Fog-enabled Federated Learning with Mutual Knowledge Distillation
Yusuke Yamasaki, Hideki Takase · 2023
Federated learning (FL) is a promising technology for achieving privacy-preserving distributed learning. Although most existing studies on FL have provided a single global model for all clients, the resulting single model cannot handle heterogeneous local environments. Therefore, we propose a novel FL framework called fog-enabled federated learning with mutual knowledge distillation (F2MKD), in which the server and each client are ascribed to distinctive models. The remarkable feature of F2MKD is the data collection at the fog servers while ensuring data confidentiality among the fog servers. This study paves the new way for client-friendly, collaborative distributed learning. The code is available at https://github.com/d3-ai/F2MKD.