Abstract Personalized Federated Learning (PFL) aims to train machine learning models on
decentralized, heterogeneous data while preserving user privacy. This research survey …

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 …

With the rapid spread of the Internet of Things (IoT), smart applications and services become
increasingly crucial, making them an easily accessible source of personally identifiable …

With the rapid growth of distributed learning and workflow orchestration, Federated Edge
Learning has emerged as a solution, enabling multiple edge devices to collaboratively train …

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