Federated learning is a distributed learning framework that operates effectively over wireless
networks. It enables devices to collaboratively train a model over wireless links by sharing …

Federated learning (FL) is a distributed learning framework where users train a global model
by exchanging local model updates with a server instead of raw datasets, preserving data …

Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge
learning with distributed data while ensuring data privacy. However, the traditional FL with …

Federated learning (FL) is a promising distributed framework that a server leverages a large
number of clients to collaboratively learn a model while keeping the privacy of their local …

Federated Learning (FL) enables clients to share learning parameters instead of local data,
reducing communication overhead. Traditional wireless networks face latency challenges …

The federated edge learning (FEEL) framework is a popular approach for privacy-preserving
collaborative model training, where edge devices and the server exchange learning updates …

With the rise of the Internet of Things (IoT) and 5G networks, edge computing addresses
critical limitations in cloud computing's quality of service. Machine learning (ML) has become …