Federated Learning (FL) has gained prominence as a method for training models on edge
computing devices, enabling the preservation of data privacy by eliminating the need to …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

Federated learning (FL) is a privacy-preserving paradigm that collaboratively train machine
learning models with distributed data stored in different silos without exposing sensitive …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

Federated learning (FL) provides a privacy-preserving solution for distributed machine
learning tasks. One challenging problem that severely damages the performance of FL …

Data privacy has become an increasingly important concern in real-world big data
applications such as machine learning. To address the problem, federated learning (FL) has …

Federated learning, which allows multiple client devices in a network to jointly train a
machine learning model without direct exposure of clients' data, is an emerging distributed …

Federated learning (FL) enhances data privacy with collaborative in-situ training on
decentralized clients. Nevertheless, FL encounters challenges due to non-independent and …

When data privacy is imposed as a necessity, Federated learning (FL) emerges as a
relevant artificial intelligence field for developing machine learning (ML) models in a …

Federated Learning (FL) proposed in recent years has received significant attention from
researchers in that it can enable multiple clients to cooperatively train global models without …