Federated Learning (FL) enables collaborative model training across edge devices while
preserving data locally. Deploying FL faces challenges due to device heterogeneity. Using …

Federated Learning (FL) is a distributed Machine Learning paradigm that allows multiple
clients to collaboratively train a model under the control of a central server while keeping …

Federated Learning (FL) is an emerging paradigm for training machine learning models
across decentralized edge devices, ensuring data privacy and reducing computational tasks …

Federated learning (FL) has emerged as a promising solution to address the challenges
posed by data silos and the need for global data fusion. It offers a distributed machine …

Federated learning (FL) enables distributed resource-constrained devices to jointly train
shared models while keeping the training data local for privacy purposes. Vertical FL (VFL) …

Federated learning (FL) is one of most promising distributed machine learning frameworks
to strengthen data privacy and security by making. Specifically, in the FL, the multiple clients …

Federated learning (FL) has recently emerged as an attractive distributed machine learning
paradigm for harnessing the distributed data in edge computing. Its salient feature is that the …

In the realm of machine learning (ML) systems featuring client-host connections, the
enhancement of privacy security can be effectively achieved through federated learning (FL) …

Federated Learning (FL) has been a promising paradigm in distributed machine learning
that enables in-situ model training and global model aggregation. While it can well preserve …

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in
machine learning training. This approach allows a variety of mobile devices to …