most citedAn Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy

2 citations · 5 across the 3 of their papers we have counts for

collaborators

3 papers

stat.CO2025★ 1 cited

Fairmetrics: An R package for group fairness evaluation

Benjamin Smith, Jianhui Gao, Jessica Gronsbell

Fairness is a growing area of machine learning (ML) that focuses on ensuring models do not produce systematically biased outcomes for specific groups, particularly those defined by…

cs.LG2025★ 2 cited

An Empirical Study of the Impact of Federated Learning on Machine Learning Model Accuracy

Haotian Yang, Zhuoran Wang, Benson Chou +4

Federated Learning (FL) enables distributed ML model training on private user data at the global scale. Despite the potential of FL demonstrated in many domains, an in-depth view o…

cs.LG2024★ 2 cited

What is Fair? Defining Fairness in Machine Learning for Health

Jianhui Gao, Benson Chou, Zachary R. McCaw +4

Ensuring that machine learning (ML) models are safe, effective, and equitable across all patients is critical for clinical decision-making and for preventing the amplification of e…