A Locally Penalized Cross-Estimate Federated Method with Guarantees for Constrained Personalized Learning

Federated learning (FL) is a communication-efficient framework for solving distributed optimization problems. Standard FL methods aggregate local updates into a single global model that is shared by all agents, effectively imposing consensus. Under heterogeneous local constraints, however, a common