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

Owing to the nature of privacy protection, feder-ated recommender systems (FedRecs) have
garnered increasing interest in the realm of on-device recommender systems. However …

Federated Learning (FL) allows clients to form a consortium to train a global model under
the orchestration of a central server while keeping data on the local client without sharing it …

In the rapidly evolving landscape of distributed learning strategies, Federated Learning (FL)
stands out for its features such as model training on resource-constrained edge devices and …

Federated learning (FL), a novel distributed machine learning (DML) approach, has been
widely adopted to train deep neural networks (DNNs), over massive data in edge computing …

Federated Learning (FL) has emerged as a prominent paradigm for distributed machine
learning, crucial for mission-critical applications such as autonomous driving and smart …

Federated learning (FL) enables distributed learning across edge devices while protecting
data privacy. However, the learning accuracy decreases due to the heterogeneity of devices' …

With the growing concerns regarding user data privacy, Federated Recommender System
(FedRec) has garnered significant attention recently due to its privacy-preserving …

Recently, federated learning (FL) as a new learning paradigm allows multi-party to
collaboratively train a shared global model with privacy protection. However, vanilla FL …

Federated learning (FL) has gained widespread attention as a distributed machine learning
(ML) technique that offers data protection when training on local devices. Unlike …