Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning

A recent study introduces a novel approach to federated ridge regression, demonstrating that iterative communication between clients and a central server is unnecessary for achieving exact recovery of the centralized solution. By aggregating sufficient statistics from clients in a single transmission, the server can reconstruct the global solution through matrix inversion, significantly reducing communication overhead.

Recent research highlights a new type of attack on Federated Learning (FL) that compromises the fairness of trained models, revealing that controlling just one client can skew performance distributions across various attributes. This raises concerns about the integrity of models in sensitive applications where fairness is critical.