Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Quantum Federated Learning (QFL) recently becomes a promising approach with the
potential to revolutionize Machine Learning (ML). It merges the established strengths of …

Effective transfer learning for molecular property prediction has shown considerable strength
in addressing insufficient labeled molecules. Many existing methods either disregard the …

Fairness in federated learning has emerged as a critical concern, aiming to develop an
unbiased model for any special group (eg, male or female) of sensitive features. However …

This paper explores the multifaceted consequences of federated unlearning (FU) with data
heterogeneity. We introduce key metrics for FU assessment, concentrating on verification …

Federated learning achieves effective performance in modeling decentralized data. In
practice client data are not well-labeled which makes it potential for federated unsupervised …

To address unfairness issues in federated learning (FL), contemporary approaches typically
use frequent model parameter updates and transmissions between the clients and server. In …

Contribution evaluation in federated learning (FL) has become a pivotal research area due
to its applicability across various domains, such as detecting low-quality datasets …

Under the landscape of climate change, carbon management has emerged as an imperative
global task in recent years, especially within the power sector, a primary emitter of …

Fairness has become an important concern in Federated Learning (FL). An unfair model that
performs well for some clients while performing poorly for others can reduce the willingness …