  In heterogeneous scenarios where the data distribution amongst the Federated
Learning (FL) participants is Non-Independent and Identically distributed
(Non-IID), FL suffers from the well known problem of data heterogeneity. This
leads the performance of FL to be significantly degraded, as the global model
tends to struggle to converge. To solve this problem, we propose Differentially
Private Synthetic Data Aided Federated Learning Using Foundation Models
(DPSDA-FL), a novel data augmentation strategy that aids in homogenizing the
local data present on the clients' side. DPSDA-FL improves the training of the
local models by leveraging differentially private synthetic data generated from
foundation models. We demonstrate the effectiveness of our approach by
evaluating it on the benchmark image dataset: CIFAR-10. Our experimental
results have shown that DPSDA-FL can improve class recall and classification
accuracy of the global model by up to 26% and 9%, respectively, in FL with
Non-IID issues.
