Trustworthy artificial intelligence (TAI) has proven invaluable in curbing potential negative
repercussions tied to AI applications. Within the TAI spectrum, federated learning (FL) …

Abstract Mobile Edge Computing (MEC) and Federated Learning (FL) have recently
attracted considerable interest for their potential applications across diverse domains. MEC …

Federated Learning (FL) is a technique that can learn a global machine-learning model at a
central server by aggregating locally trained models. This distributed machine-learning …

Federated learning (FL) is considered a de facto standard for privacy preservation in AI
environments because it does not require data to be aggregated in some central place to …

Federated Learning (FL) provides a framework for collaborative training of deep learning
models while preserving data privacy by avoiding sharing the training data. However, recent …

The multi-volume set of LNCS books with volume numbers 15059 up to 15147 constitutes
the refereed proceedings of the 18th European Conference on Computer Vision, ECCV …

Distributed machine learning, such as federated learning, protects privacy by collecting
gradients instead of training data. Recent studies have shown that gradient leakage attacks …

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic
factors associated with specific traits. However, ethical constraints prevent the direct …

The distributed training feature and data heterogeneity in federated learning (FL) render it
susceptible to various threats, in which the backdoor attack stands out as the most …

Federated learning (FL) has emerged as one of the de-facto privacy-preserving paradigms
that can effectively work with decentralized data sources (eg, hospitals) without acquiring …