Client Invariant
Client-invariant representation learning in federated learning aims to build robust global models from diverse, decentralized data sources without compromising individual client privacy. Current research focuses on developing techniques like feature diversification, selective knowledge sharing, and feature disentanglement to mitigate the negative impact of data heterogeneity across clients, improving model accuracy and generalization. These methods leverage various model architectures and algorithms to extract shared, client-invariant features while preserving essential client-specific information, addressing a critical challenge in the practical deployment of federated learning. The resulting advancements enhance the reliability and performance of federated models across diverse applications.