With the rapid growth in the volume of data sets, models, and devices in the domain of deep
learning, there is increasing attention on large-scale distributed deep learning. In contrast to …

To address the communication burden and privacy concerns associated with the centralized
server in Federated Learning (FL), Decentralized Federated Learning (DFL) has emerged …

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

Recently, federated learning (FL) has emerged as a popular technique for edge AI to mine
valuable knowledge in edge computing (EC) systems. To boost the performance of AI …

The massive data from mobile and embedded devices have huge potential for training
machine learning models. Decentralized machine learning (DML) can avoid the inherent …

Disability management in information systems refers to the process of ensuring that digital
technologies and applications are designed to be accessible and usable by individuals with …

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-
empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to …

Data generated at the network edge can be processed locally by leveraging the emerging
technology of Federated Learning (FL). However, non-local data will lead to degradation of …

Decentralized federated learning (DFL) is a promising technique to enable distributed
machine learning over edge nodes without relying on a centralized parameter server …

Federated learning (FL) based on the centralized design faces both challenges regarding
the trust issue and a single point of failure. To alleviate these issues, blockchain-aided …