Federated learning (FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

Federated learning~(FL) facilitates the training and deploying AI models on edge devices.
Preserving user data privacy in FL introduces several challenges, including expensive …

Federated Learning (FL) is a type of distributed machine learning, which avoids sharing
privacy and sensitive data with a central server. Despite the advances in FL, current …

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) is an attractive distributed learning paradigm supporting real-time
continuous learning and client privacy by default. In most FL approaches, all edge clients …

Federated learning (FL) enables multiple devices to collaboratively train a shared model
while preserving data privacy. However, despite its emerging applications in many areas …

Federated Learning (FL) has emerged as a promising solution in Edge Computing (EC)
environments to process the proliferation of data generated by edge devices. By …

Federated learning (FL) is a distributed model training paradigm that preserves clients' data
privacy. It has gained tremendous attention from both academia and industry. FL …

Federated Learning (FL) offers a novel distributed machine learning context whereby a
global model is collaboratively learned through edge devices without violating data privacy …

Federated Learning (FL) enables model development by leveraging data distributed across
numerous edge devices without transferring local data to a central server. However, existing …