Abstract Federated Learning (FL) enhances Artificial Intelligence (AI) applications by
enabling individual devices to collaboratively learn shared models without uploading local …

Recent years have witnessed a thriving growth of computing facilities connected at the
network edge, cultivating edge networks as a fundamental infrastructure for supporting …

Transformer-based models have unlocked a plethora of powerful intelligent applications at
the edge, such as voice assistant in smart home. Traditional deployment approaches offload …

On-device Deep Neural Network (DNN) training has been recognized as crucial for privacy-
preserving machine learning at the edge. However, the intensive training workload and …

Federated Learning (FL) has emerged as a fundamental learning paradigm to harness
massive data scattered at geo-distributed edge devices in a privacy-preserving way. Given …

Large language models (LLMs) have unlocked a plethora of powerful applications at the
network edge, such as intelligent personal assistants. Data privacy and security concerns …

Federated Learning (FL) has recently received extensive attention in enabling privacy-
preserving edge AI services for Human Activity Recognition (HAR). However, users' mobile …

Federated learning (FL) provides a collaborative framework that enables intelligent
networking devices to train a shared model without the need to share local data. FL has …

Federated Learning (FL) has emerged as a promising decentralized paradigm for training
machine learning models across distributed devices, ushering in a new era of collaborative …

In recent times, Federated Learning (FL) has played a vital role in real-time applications by
collaboratively learning a shared model across massive end devices without exchanging …