Abstract The Federated Learning (FL) paradigm is known to face challenges under
heterogeneous client data. Local training on non-iid distributed data results in deflected …

Federated Learning (FL) aims to learn a global model from distributed users while protecting
their privacy. However, when data are distributed heterogeneously the learning process …

Federated learning (FL) allows multiple clients to collectively train a high-performance
global model without sharing their private data. However, the key challenge in federated …

Federated Learning (FL) is an emerging distributed learning paradigm under privacy
constraint. Data heterogeneity is one of the main challenges in FL, which results in slow …

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) 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) offers a solution to train a global machine learning model while still
maintaining data privacy, without needing access to data stored locally at the clients …

Federated learning (FL) is a distributed learning paradigm that enables multiple clients to
collaboratively learn a shared global model. Despite the recent progress, it remains …

Federated learning (FL) has recently attracted increasing attention from academia and
industry, with the ultimate goal of achieving collaborative training under privacy and …

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 …