Federated learning has emerged as a promising approach for collaborative model training
across distributed devices. Federated learning faces challenges such as Non-Independent …

Efficient collaboration between collaborative machine learning and wireless communication
technology, forming a Federated Edge Learning (FEEL), has spawned a series of next …

Unification of classification and regression is a major challenge in machine learning and has
attracted increasing attentions from researchers. In this article, we present a new idea for this …

This paper delves into federated class-incremental learning (FCiL), where new classes
appear continually or even privately to local clients. However, existing FCiL methods suffer …

Healthcare systems based on the Internet of Things have an increasing demand for health
sensing technology. To manage the data collected and sampled by medical devices …

Federated learning (FL) is a distributed approach to train machine learning models without
disclosing private data from participating clients to a central server. Nevertheless, FL training …

In the realm of targeted advertising, the demand for precision is paramount, and the
traditional centralized machine learning paradigm fails to address this necessity effectively …

In this letter, we investigate the training latency minimization problem for a multi-task
federated learning (FL) framework with model decomposition over wireless communication …

Binary decomposition of a multi-class classification problem is a widely used method in the
field of machine learning, which involves using an ensemble of binary classifiers to …

The efficient management of traffic flow on highways is crucial for ensuring safety, reducing
congestion, and optimizing transportation infrastructure. However, real-world scenarios often …