Feb 9, 2023 · Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility. To prove our lower bound, we consider the case of mean ...
Abstract. The ubiquity of distributed machine learning (ML) in sensitive public domain applications calls for algorithms that protect data privacy, ...
Jul 23, 2023 · Our analysis exhibits a fundamental trade-off between privacy, robustness, and utility. To prove our lower bound, we consider the case of mean ...
On the Privacy-Robustness-Utility. Trilemma in Distributed Learning. Youssef ... Lower Bounds: we show that privacy and robustness induce a coupled cost.
This work presents the first tight analysis of the error incurred by any algorithm ensuring robustness against a fraction of adversarial machines, ...
May 29, 2023 · We precisely characterize the privacy-robustness-utility trilemma in distributed learning. Specifically, we present the first tight analysis of ...
On the Privacy-Robustness-Utility Trilemma in Distributed Learning. Y Allouah, R Guerraoui, N Gupta, R Pinot, J Stephan. ICML 2023, 2023. 19, 2023. Robust ...
Rafael Pinot · Robust Distributed Learning: Tight Error Bounds and Breakdown Point under Dat…a Heterogeneity · On the Privacy-Robustness-Utility Trilemma in ...
Robust distributed learning: tight error bounds and breakdown point under data ... On the privacy-robustness-utility trilemma in distributed learning · Author ...
Robustness, Efficiency, or Privacy: Pick Two in Machine Learning ... The success of machine learning (ML) applications relies on vast datasets and distributed ...