Federated Learning (FL) is a promising framework for performing privacy-preserving,
distributed learning with a set of clients. However, the data distribution among clients often …

This paper explores the potential for communication-efficient federated learning (FL) in
modern distributed systems. FL is an emerging distributed machine learning technique that …

Personalized federated learning (PFL), as a novel federated learning (FL) paradigm, is
capable of generating personalized models for heterogenous clients. Combined with a meta …

Federated Learning (FL) is a distributed learning paradigm that collaboratively learns a
global model across multiple clients with data privacy-preserving. Although many FL …

In federated learning, a strong global model is collaboratively learned by aggregating
clients' locally trained models. Although this precludes the need to access clients' data …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Federated learning is an important privacy-preserving multi-party learning paradigm,
involving collaborative learning with others and local updating on private data. Model …

Abstract Model heterogeneous federated learning is a realistic and challenging problem.
However, due to the limitations of data collection, storage, and transmission conditions, as …

Federated learning is an emerging learning paradigm where multiple clients collaboratively
train a machine learning model in a privacy-preserving manner. Personalized federated …

Federated learning (FL) emerges as a decentralized learning framework which trains
models from multiple distributed clients without sharing their data to preserve privacy …