1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2026
Private Vertical Federated Inference for Time-Series
Lucas Fenaux, Larris Xie, Aditya Bang +3
Institutions may benefit from collaborative inference on time-series data. In settings where privacy is necessary, multi-party computation (MPC) is a straightforward approach to pr…
cs.LG2026★ 1 cited
Hammer and Anvil: Toward a Theory of Backdoors in Federated Learning
Lucas Fenaux, Zheng Wang, Jacob Yan +2
Federated Learning (FL) enables distributed model training but is vulnerable to backdoor attacks, where malicious clients embed attacker-controlled behaviors into the global model.…
cs.LG2025
On the Trade-Off Between Transparency and Security in Adversarial Machine Learning
Lucas Fenaux, Christopher Srinivasa, Florian Kerschbaum
Transparency and security are both central to Responsible AI, but they may conflict in adversarial settings. We investigate the strategic effect of transparency for agents through…