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) is a collaborative machine learning paradigm where network-edge
clients train a global model under the orchestration of a central server. Unlike traditional …

Federated Learning (FL) enables collaborations among clients for train machine learning
models while protecting their data privacy. Existing FL simulation platforms that are …

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) 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 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) 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) supports training models on geographically distributed devices.
However, traditional FL systems adopt a centralized synchronous strategy, putting high …

Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

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