Federated learning (FL) has drawn increasing attention owing to its potential use in large-
scale industrial applications. Existing FL works mainly focus on model homogeneous …

Federated learning (FL) is a novel distributed machine learning paradigm that enables
participants to collaboratively train a centralized model with privacy preservation by …

Federated learning has emerged as an important distributed learning paradigm, which
normally involves collaborative updating with others and local updating on private data …

Federated learning enables multiple parties to collaboratively train a machine learning
model without communicating their local data. A key challenge in federated learning is to …

Federated Learning (FL) is a machine learning paradigm that allows decentralized clients to
learn collaboratively without sharing their private data. However, excessive computation and …

Federated learning (FL) enables multiple clients to jointly train high-performance deep
learning models while maintaining the training data locally. However, it is challenging to …

Unsupervised representation learning has achieved outstanding performances using
centralized data available on the Internet. However, the increasing awareness of privacy …

Self-supervised learning (SSL) is capable of learning remarkable representations from
centrally available data. Recent works further implement federated learning with SSL to …

Abstract The Federated Averaging (FedAvg) algorithm, which consists of alternating
between a few local stochastic gradient updates at client nodes, followed by a model …

The collection and curation of large-scale medical datasets from multiple institutions is
essential for training accurate deep learning models, but privacy concerns often hinder data …