3 papers
cs.LG2026
Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
Collaborative machine learning (CML) enables multiple clients to train a global model jointly in a data-distributed setting. To address data privacy and communication efficiency, o…
math.OC2026
Low-Order Explicit Hessian Imitation Method for Large-Scale Supervised Machine Learning
Yunlang Zhu, Lingjun Guo, Zahra Khatti +4
An algorithm is proposed for solving optimization problems arising in neural network training for supervised learning. The unique feature of the algorithm is the use of an auxiliar…
cs.LG2025
Using Synthetic Data to Mitigate Unfairness and Preserve Privacy in Collaborative Machine Learning
Chia-Yuan Wu, Frank E. Curtis, Daniel P. Robinson
In distributed computing environments, collaborative machine learning enables multiple clients to train a global model collaboratively. To preserve privacy in such settings, a comm…