5 citations · 8 across the 7 of their papers we have counts for
4 papers · 1 filter
AGIC: Approximate Gradient Inversion Attack on Federated Learning
Jin Xu, Chi Hong, Jiyue Huang +2
Federated learning is a private-by-design distributed learning paradigm where clients train local models on their own data before a central server aggregates their local updates to…
Is Shapley Value fair? Improving Client Selection for Mavericks in Federated Learning
Jiyue Huang, Chi Hong, Lydia Y. Chen +1
Shapley Value is commonly adopted to measure and incentivize client participation in federated learning. In this paper, we show -- theoretically and through simulations -- that Sha…
End-to-End Learning from Noisy Crowd to Supervised Machine Learning Models
Taraneh Younesian, Chi Hong, Amirmasoud Ghiassi +2
Labeling real-world datasets is time consuming but indispensable for supervised machine learning models. A common solution is to distribute the labeling task across a large number…
Online Label Aggregation: A Variational Bayesian Approach
Chi Hong, Amirmasoud Ghiassi, Yichi Zhou +2
Noisy labeled data is more a norm than a rarity for crowd sourced contents. It is effective to distill noise and infer correct labels through aggregation results from crowd workers…