Federated Learning in Non-IID Settings Aided by Differentially Private Synthetic Data. (arXiv:2206.00686v1 [cs.LG])

Federated learning (FL) is a privacy-promoting framework that enables
potentially large number of clients to collaboratively train machine learning
models. In a FL system, a server coordinates the collaboration by collecting
and aggregating clients' model updates while the clients' data remains local
and private. A major challenge in federated learning arises when the local data
is heterogeneous -- the setting in which performance of the learned global
model may deteriorate significantly compared to the scenario where the data is
identically …

data lg non settings synthetic data