An Element-Wise Weights Aggregation Method for Federated Learning

arXiv:2404.15919v1 Announce Type: new
Abstract: Federated learning (FL) is a powerful Machine Learning (ML) paradigm that enables distributed clients to collaboratively learn a shared global model while keeping the data on the original device, thereby preserving privacy. A central challenge in FL is the effective aggregation of local model weights from disparate and potentially unbalanced participating clients. Existing methods often treat each client indiscriminately, applying a single proportion to the entire local model. However, it is empirically advantageous for each …

abstract aggregation arxiv challenge cs.cv cs.lg data distributed element federated learning global learn machine machine learning paradigm privacy type while wise