Mobile-edge computing (MEC) has been envisioned as a promising paradigm to handle the
massive volume of data generated from ubiquitous mobile devices for enabling intelligent …

Abstract Asynchronous Federated Learning (AFL) has been introduced to improve the
efficiency of FL by reducing the latency of Machine Learning (ML) model aggregation …

Federated learning allows a group of distributed clients to train a common machine learning
model on private data. The exchange of model updates is managed either by a central entity …

The proliferation of Internet of Things (IoT) devices, generating massive amounts of
heterogeneous distributed data, has pushed toward edge cloud computing as a promising …

It is difficult to manage massive amounts of data in an overlying environment with a single
server. Therefore, it is necessary to comprehend the security provisions for erratic data in a …

Abstract The Internet of Things (IoT) has gained substantial attention in both academic
research and real-world applications. The proliferation of interconnected devices across …

Federated learning (FL) aims to collaboratively train a global model while ensuring client
data privacy. However, FL faces challenges from the non-IID data distribution among clients …

With the development of the power internet of things, the traditional centralized computing
pattern has been difficult to apply to many power business scenarios, including power load …

Decentralised learning has recently gained traction as an alternative to federated learning in
which both data and coordination are distributed over its users. To preserve the …

Federated and decentralized machine learning leverage end-user devices for privacy-
preserving training of models at lower operating costs than within a data center. In a round of …