Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups

We study the problems of differentially private federated online prediction from experts against both stochastic adversaries and oblivious adversaries. We aim to minimize the average regret on \(m\) clients working in parallel over time horizon \(T\) with explicit differential privacy (DP) guarantee...

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Veröffentlicht in:	arXiv.org 2024-09
Hauptverfasser:	Gao, Fengyu, Huang, Ruiquan, Yang, Jing
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Clients Constraints Logarithms Lower bounds Privacy
Online-Zugang:	Volltext
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