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 feasible model may be overly restrictive or may not exist. Existing constrained FL methods largely retain this shared-model structure and therefore do not directly address personalization under heterogeneous agent-specific feasible sets. We study a constrained personalized FL problem that assigns a distinct feasible model to each agent while coupling the models through a collaborative objective...
| Authors | Mohammadjavad Ebrahimi, Daniel Burbano, Farzad Yousefian |
|---|---|
| Primary category | cs.LG |
| Published | 20 March 2026 |
| Last updated at source | 2 October 2026 |
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