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

As data are increasingly being stored in different silos and societies becoming more aware
of data privacy issues, the traditional centralized training of artificial intelligence (AI) models …

In recent years, data has become one of the most important resources in the digital
economy. Unlike traditional resources, the digital nature of data makes it difficult to value …

Recent years have witnessed a surge of interest in develo** trustworthy methods to
evaluate the value of data in many real-world applications (eg, collaborative machine …

How to ensure fairness is an important topic in federated learning (FL). Recent studies have
investigated how to reward clients based on their contribution (collaboration fairness), and …

The growing literature of Federated Learning (FL) has recently inspired Federated
Reinforcement Learning (FRL) to encourage multiple agents to federatively build a better …

With the growing demand for the right to be forgotten, there is an increasing need for
machine learning models to forget sensitive data and its impact. To address this, the …

Data valuation is a powerful framework for providing statistical insights into which data are
beneficial or detrimental to model training. Many Shapley-based data valuation methods …

Data valuation arises as a non-trivial challenge in real-world use cases such as
collaborative machine learning, federated learning, trusted data sharing, data marketplaces …

Data valuation in machine learning (ML) is an emerging research area that studies the worth
of data in ML. Data valuation is used in collaborative ML to determine a fair compensation …