A Survey on Personalization Capabilities in Federated Learning
A Survey on Personalization in Federated Learning Abstract The goal of federated learning is to obtain a shared global model that all nodes can use. However, due to Non-IID data distributions, local models trained solely on local data often outperform the global model, which makes these nodes reluctant to participate in the federation process. This post introduces techniques currently used to personalize the global model so as to improve its performance on individual nodes. ...