Abstract Virtual Rehabilitation (VRehab) is a promising approach to improving the physical
and mental functioning of patients living in the community. The use of VRehab technology …

The inevitable private information in legal data necessitates legal artificial intelligence to
study privacy-preserving and decentralized learning methods. Federated learning (FL) has …

Federated learning (FL) is usually performed on resource-constrained edge devices, eg,
with limited memory for the computation. If the required memory to train a model exceeds …

In response to the increasing volume and sensitivity of data, traditional centralized
computing models face challenges, such as data security breaches and regulatory hurdles …

Internet of Things (IoT) interconnects a massive amount of devices, generating
heterogeneous data with diverse characteristics. IoT data emerges as a vital asset for data …

This work investigates fine-grained data distribution in real-world federated learning (FL)
applications, wherein training samples are distributed across multiple regions, and different …

In the field of heterogeneous federated learning (FL), the key challenge is to efficiently and
collaboratively train models across multiple clients with different data distributions, model …

Distributed learning is commonly used for accelerating model training by harnessing the
computational capabilities of multiple-edge devices. However, in practical applications, the …

The rise of mobile devices with abundant sensory data and local computing capabilities has
driven the trend of federated learning (FL) on these devices. And personalized FL (PFL) …

Federated Learning (FL) is a promising paradigm that offers significant advancements in
privacy-preserving, decentralized machine learning by enabling collaborative training of …