UCB, wherein agents cooperatively minimize the group regret under the coordination of a central server.

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

Human perception of the empirical world involves recognizing the diverse appearances, or'modalities', of underlying objects.

Human perception of the empirical world involves recognizing the diverse appearances, or'modalities', of underlying objects.

Federated Learning (FL) is an emerging collaborative machine learning framework where multiple clients train the global model without sharing their own datasets.

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