In this paper, we present the first stepsize schedule for Newton method resulting in fast
global and local convergence guarantees. In particular, we a) prove an $\mathcal O\left …

Personalized federated learning (PFL) aims to produce the greatest personalized model for
each client to face an insurmountable problem--data heterogeneity in real FL systems …

Abstract Personalized Federated Learning (PFL) is proposed to find the greatest
personalized models for each client. To avoid the central failure and communication …

Personalization in federated learning (FL) functions as a coordinator for clients with high
variance in data or behavior. Ensuring the convergence of these clients' models relies on …

Federated Learning (FL) is gaining widespread interest for its ability to share knowledge
while preserving privacy and reducing communication costs. Unlike Centralized FL …

In Federated Learning (FL), the distributed nature and heterogeneity of client data present
both opportunities and challenges. While collaboration among clients can significantly …

We study the personalized federated learning problem under asynchronous updates. In this
problem, each client seeks to obtain a personalized model that simultaneously outperforms …

Non-iid data is prevalent in real-world federated learning problems. Data heterogeneity can
come in different types in terms of distribution shifts. In this work, we are interested in the …

We propose a novel distributed method for non-convex optimization problems with coupling
equality and inequality constraints. This method transforms the optimization problem into a …

The emerging Federated Learning (FL) permits all workers (eg, mobile devices) to
cooperatively train a model using their local data at the network edge. In order to avoid the …