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

Sequences of millions of bytes are ubiquitous; for example, music, image, or video files typically consist of multiple megabytes.

Federated Learning (FL) allows machine learning models to train locally on individual mobile devices, synchronizing model updates via a shared server.

To mitigate the negative effect of data heterogeneity (non-IIDness), two common approaches are clustering and personalization.

This urges the development of more computation and communication efficient optimization algorithms.

Due to its distributed nature, many studies have shown that it is vulnerable to backdoor attacks.