It is regarded as a promising distributed and privacy-preserving method.

Federated learning has become a pivotal distributed learning paradigm, involving collaborative model updates across multiple nodes with private data.

However, due to the heterogeneity between the clients' data distributions, the model obtained through the use of FL algorithms may perform poorly on some client's data.

Federated learning (FL) typically faces data heterogeneity, i.e., distribution shifting among clients. Sharing clients' information has shown great potentiality in mitigating data heterogeneity, yet incurs a dilemma in preserving privacy and promoting

Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.

We analytically characterize the convergence behavior of MTGC under general non-convex settings, overcoming challenges associated with couplings between correction terms.

This has motivated numerous studies aiming to reduce the variance and improve convergence of FL on non-IID data [6, 9, 14, 17, 19, 30]. On another note, constraints on communication resources and therefore on the number of clients that may participate in training additionally complicate implementation of FL schemes.

A vast majority of prior work on robustness however assumes data homogeneity, i.e., local datasets are generated from the same distribution.