Accelerating Semi-Asynchronous Federated Learning

arXiv:2402.10991v1 Announce Type: new
Abstract: Federated Learning (FL) is a distributed machine learning paradigm that allows clients to train models on their data while preserving their privacy. FL algorithms, such as Federated Averaging (FedAvg) and its variants, have been shown to converge well in many scenarios. However, these methods require clients to upload their local updates to the server in a synchronous manner, which can be slow and unreliable in realistic FL settings. To address this issue, researchers have developed …

abstract algorithms arxiv asynchronous converge cs.ai cs.lg data distributed federated learning machine machine learning paradigm privacy train type updates variants