Xiaolu Wang

Achieving Linear Speedup in Asynchronous Federated Learning with Heterogeneous Clients

2024 · arXiv (Cornell University)

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 …