Federated learning (FL) over mobile devices is a promising distributed learning paradigm for
various mobile applications. However, practical deployment of FL over mobile devices is …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
learn a powerful global model by aggregating local training. However, the performance of …

Federated Learning (FL) is a distributed learning paradigm that can learn a global or
personalized model from decentralized datasets on edge devices. However, in the computer …

Federated learning (FL) is a promising strategy for performing privacy-preserving, distributed
learning with a network of clients (ie, edge devices). However, the data distribution among …

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), an emerging distributed machine learning paradigm, in conflux with
edge computing is a promising area with novel applications over mobile edge devices. In …

Federated Learning (FL) has emerged as a de facto machine learning area and received
rapid increasing research interests from the community. However, catastrophic forgetting …

Federated learning (FL) is a powerful technology that enables collaborative training of
machine learning models without sharing private data among clients. The fundamental …

Federated learning (FL) has been widely deployed in edge computing scenarios. However,
FL-related technologies are still facing severe challenges while evolving rapidly. Among …

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