5 citations · 21 across the 10 of their papers we have counts for
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cs.LG2025
TS-Inverse: A Gradient Inversion Attack Tailored for Federated Time Series Forecasting Models
Caspar Meijer, Jiyue Huang, Shreshtha Sharma +2
Federated learning (FL) for time series forecasting (TSF) enables clients with privacy-sensitive time series (TS) data to collaboratively learn accurate forecasting models, for exa…
cs.LG2022★ 1 cited
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…
cs.LG2021★ 5 cited
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…