In the era of big data, deep learning models play a crucial role in identifying underlying patterns within data. However, the need for large volumes of training data, often scattered across various organizations with privacy constraints, poses a significant challenge. Federated Learning (FL) addresses this by enabling the collaborative training of models without sharing the underlying data. Despite its promise, FL encounters challenges with model privacy leakage and computational overhead, particularly when dealing with non-identically distribu...
