The paper introduces Fed‑LSVI, a federated online reinforcement learning algorithm that uses linear function approximation in episodic Markov decision processes. It achieves a regret bound of ≥O(√{Md^3H^4T}) while only exchanging compressed sufficient statistics, thereby meeting privacy constraints. The method reduces communication cost to logarithmic in the number of episodes, a marked improvement over previous approaches that required linear communication.
By Zihang Liang, Haochen Zhang, Lingzhou Xue
arXiv:2607. 08012v1 Announce Type: cross Abstract: This paper studies an online variant of the assistance games framework, where an informed agent and an uninformed agent repeatedly interact over $T$ timesteps to optimize a common reward function.
By Nivasini Ananthakrishnan, Mark Bedaywi, Michael I. Jordan, Stuart Russell, Nika Haghtalab
arXiv:2609.14778v1 Announce Type: new
Abstract: We propose a decentralized privacy-preserving learning algorithm in which each agent holds a single private sample and a shared model. Samples are lear...
By Erkan Bayram, Mohamed-Ali Belabbas, Tamer Ba\c{s}ar
arXiv:2610.29575v1 Announce Type: new
Abstract: We consider online prediction from experts, a fundamental problem in machine learning, under differential privacy constraints. Existing private algorit...
By Bo Li, Peng Ye
arXiv:2608.28198v1 Announce Type: new
Abstract: Privacy-preserving learning is often motivated by the idea that protecting users' data can preserve trust and thus participation, improving utility in...
By Uddalak Mukherjee, Edwige Cyffers, Yann Chevaleyre
arXiv:2608. 06520v1 Announce Type: new Abstract: We study online cooperative control of a multi-agent system under Byzantine attacks.
By Ximing Sun, Yue Wang
arXiv:2608. 16699v1 Announce Type: cross Abstract: Motivated by modern marketplaces, where the platform or the seller routinely gathers detailed user profiles, we study a novel learning theoretic model that simultaneously involves information and mechanism design.
By Maria-Florina Balcan, Tejas Pagare, Karan Singh
arXiv:2503. 01701v2 Announce Type: replace-cross Abstract: Most microeconomic models of interest involve optimizing a piecewise linear function.
By Francesco Bacchiocchi, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
The paper investigates how randomized exploration in bandit learning can be compromised when an adversarial agent gains access to another agent’s exploratory signals. In a two‑player strongly monotone setting, the deceptive agent couples its actions with the leaked information to steer the learning dynamics toward a new steady state, termed the deceptive Nash equilibrium (DNE). The authors prove that the deceptive bandit learning dynamics converge to an arbitrarily small neighborhood of the DNE while maintaining optimal convergence rates, even under relaxed smoothness assumptions, and illustrate the impact on costs in a resource‑allocation game.
By Michael Tang, Mahmoud Abdelgalil, Jorge I. Poveda
arXiv:2607. 14877v1 Announce Type: new Abstract: Reachability is the most fundamental logical objective, yet it is notoriously difficult to learn in reinforcement learning settings: even for Markov decision processes, PAC learning of reachability is impossible without additional assumptions.
By Ali Asadi, Krishnendu Chatterjee, Pavol Kebis
The paper investigates federated learning over multiple‑access fading channels with a multi‑antenna base station, focusing on user‑level differential privacy (DP). It shows that DP can be achieved as a natural benefit of channel noise without adding artificial noise, deriving new DP and convergence bounds for general bounded‑domain models and non‑convex loss functions. The authors further optimize beamforming and power allocation to characterize the trade‑off between privacy and convergence, providing theoretical insights and numerical validation.
By Hao Liang, Haifeng Wen, Kaishun Wu, Khaled B. Letaief, Hong Xing
arXiv:2608. 02480v1 Announce Type: cross Abstract: With AI systems gaining more access to individuals' information, it is important to protect privacy when reporting statistical answers.
By Jinwon Sohn, Veronika Ro\v{c}kov\'a