Federated learning (FL) is a distributed machine learning strategy that generates a global
model by learning from multiple decentralized edge clients. FL enables on-device training …

As data privacy increasingly becomes a critical societal concern, federated learning has
been a hot research topic in enabling the collaborative training of machine learning models …

Federated learning is a set-up in which multiple clients collaborate to solve machine
learning problems, which is under the coordination of a central aggregator. This setting also …

The federated learning technique (FL) supports the collaborative training of machine
learning and deep learning models for edge network optimization. Although a complex edge …

Privacy-preserving federated learning, as one of the privacy-preserving computation
techniques, is a promising distributed and privacy-preserving machine learning (ML) …

Distributed learning allows a group of independent data owners to collaboratively learn a
model over their data sets without exposing their private data. We present a distributed …

This paper focuses on the problem of personalized federated learning (FL) with the schema
of contrastive learning (CL), which is to implement collaborative pattern classification by …

In recent years, the extensive application of machine learning technologies has been
witnessed in various fields. However, in many applications, massive data are distributively …

Federated learning (FL) has emerged as a promising technique for preserving user privacy
and ensuring data security in distributed machine learning contexts, particularly in edge …

Crude oil price forecasting has attracted much attention due to its significance on
commodities market as well as nonlinear complexity in prediction task. Combining forecasts …