Reviving Stale Updates: Data-Free Knowledge Distillation for Asynchronous Federated Learning

A recent study on Federated Learning (FL) highlights the potential of Data-Free Knowledge Distillation to enhance Asynchronous Federated Learning (AFL). This approach allows clients to train models collaboratively without sharing raw data, addressing the challenge of synchronization overhead. By enabling independent communication among clients, AFL improves efficiency in large-scale environments. This innovation is significant as it paves the way for more scalable and effective machine learning applications, particularly in diverse and distributed settings.