In recent years, Federated Learning (FL) has gained relevance in training collaborative
models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …

Abstract Personalized Federated Learning (pFL) not only can capture the common priors
from broad range of distributed data, but also support customized models for heterogeneous …

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 shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

A central challenge in training classification models in the real-world federated system is
learning with non-IID data. To cope with this, most of the existing works involve enforcing …

The booming development of deep learning applications and services heavily relies on
large deep learning models and massive data in the cloud. However, cloud-based deep …

Edge Computing (EC) is a new architecture that extends Cloud Computing (CC) services
closer to data sources. EC combined with Deep Learning (DL) is a promising technology …

Scalability and privacy are two critical concerns for cross-device federated learning (FL)
systems. In this work, we identify that synchronous FL–cannot scale efficiently beyond a few …

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data
to collaboratively train machine learning models, without centralizing data. The cross-silo FL …