Zobrazeno 1 - 10
of 79
pro vyhledávání: '"Yemini, Michal"'
This letter studies the Friedkin-Johnsen (FJ) model with diminishing competition, or stubbornness. The original FJ model assumes that each agent assigns a constant competition weight to its initial opinion. In contrast, we investigate the effect of d
Externí odkaz:
http://arxiv.org/abs/2409.12601
In this work, we introduce the Resilient Projected Push-Pull (RP3) algorithm designed for distributed optimization in multi-agent cyber-physical systems with directed communication graphs and the presence of malicious agents. Our algorithm leverages
Externí odkaz:
http://arxiv.org/abs/2407.06541
We consider the problem of privately estimating the mean of vectors distributed across different nodes of an unreliable wireless network, where communications between nodes can fail intermittently. We adopt a semi-decentralized setup, wherein to miti
Externí odkaz:
http://arxiv.org/abs/2406.03766
Clipped stochastic gradient descent (SGD) algorithms are among the most popular algorithms for privacy preserving optimization that reduces the leakage of users' identity in model training. This paper studies the convergence properties of these algor
Externí odkaz:
http://arxiv.org/abs/2404.10995
Autor:
Ballotta, Luca, Yemini, Michal
We consider a multi-agent system where agents aim to achieve a consensus despite interactions with malicious agents that communicate misleading information. Physical channels supporting communication in cyberphysical systems offer attractive opportun
Externí odkaz:
http://arxiv.org/abs/2404.07838
Autor:
Gil, Stephanie, Yemini, Michal, Chorti, Arsenia, Nedić, Angelia, Poor, H. Vincent, Goldsmith, Andrea J.
Multi-agent cyberphysical systems enable new capabilities in efficiency, resilience, and security. The unique characteristics of these systems prompt a reevaluation of their security concepts, including their vulnerabilities, and mechanisms to mitiga
Externí odkaz:
http://arxiv.org/abs/2311.07492
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a
Externí odkaz:
http://arxiv.org/abs/2303.04075
This work considers the problem of Distributed Mean Estimation (DME) over networks with intermittent connectivity, where the goal is to learn a global statistic over the data samples localized across distributed nodes with the help of a central serve
Externí odkaz:
http://arxiv.org/abs/2303.00035
This work focuses on the problem of distributed optimization in multi-agent cyberphysical systems, where a legitimate agents' iterates are influenced both by the values it receives from potentially malicious neighboring agents, and by its own self-se
Externí odkaz:
http://arxiv.org/abs/2212.02459
We develop a resilient binary hypothesis testing framework for decision making in adversarial multi-robot crowdsensing tasks. This framework exploits stochastic trust observations between robots to arrive at tractable, resilient decision making at a
Externí odkaz:
http://arxiv.org/abs/2209.12285