Dzmitry Huba

Federated Learning with Buffered Asynchronous Aggregation

Scalability and privacy are two critical concerns for cross-device federated learning (FL) systems. In this work, we identify that synchronous FL – cannot scale efficiently beyond a few hundred clients training in parallel. It leads to diminishing returns in model performance and training speed, ana…