Achieving Linear Speedup in Asynchronous Federated Learning with
Heterogeneous Clients
Achieving Linear Speedup in Asynchronous Federated Learning with
Heterogeneous Clients
Federated learning (FL) is an emerging distributed training paradigm that aims to learn a common global model without exchanging or transferring the data that are stored locally at different clients. The Federated Averaging (FedAvg)-based algorithms have gained substantial popularity in FL to reduce the communication overhead, where each client conducts …