Federated learning (FL) is a distributed machine learning (ML) approach that enables
models to be trained on client devices while ensuring the privacy of user data. Model …

Edge intelligence is an emerging technology that enables artificial intelligence on connected
systems and devices in close proximity to the data sources. Decentralized Collaborative …

Federated learning (FL) is an efficient decentralized machine learning methodology for
processing nonindependent and identically distributed (non-IID) data due to geographical …

This paper explores constrained non-convex personalized federated learning (PFL), in
which a group of workers train local models and a global model, under the coordination of a …

Federated learning (FL) systems enable collaborative model training among industrial
Internet of Things (IIoT) devices but face significant security challenges, particularly in …

The emerging federated learning applications raise challenges of Byzantine-robustness and
communication efficiency in distributed non-convex learning over non-IID data. To address …

This paper investigates personalized federated learning, in which a group of workers are
coordinated by a server to train correlated local models, in addition to a common global …

Federated learning in machine learning (ML) allows several users to train a model without
sharing raw data. This strategy may be very helpful for training models with several data …