Zobrazeno 1 - 10
of 377
pro vyhledávání: '"Leshem, Amir"'
In this paper, we consider multi-objective reinforcement learning, which arises in many real-world problems with multiple optimization goals. We approach the problem with a max-min framework focusing on fairness among the multiple goals and develop a
Externí odkaz:
http://arxiv.org/abs/2406.07826
This paper considers a resource allocation problem where several Internet-of-Things (IoT) devices send data to a base station (BS) with or without the help of the reconfigurable intelligent surface (RIS) assisted cellular network. The objective is to
Externí odkaz:
http://arxiv.org/abs/2406.05780
Federated learning is a technique that allows multiple entities to collaboratively train models using their data without compromising data privacy. However, despite its advantages, federated learning can be susceptible to false data injection attacks
Externí odkaz:
http://arxiv.org/abs/2312.02102
Autor:
Darweesh, Rana, Yadav, Rajesh Kumar, Adler, Elior, Poplinger, Michal, Levi, Adi, Lee, Jea-Jung, Leshem, Amir, Ramasubramaniam, Ashwin, Xia, Fengnian, Naveh, Doron
Optical spectroscopy the measurement of electromagnetic spectra is fundamental to various scientific domains and serves as the building block of numerous technologies. Computational spectrometry is an emerging field that employs an array of photodete
Externí odkaz:
http://arxiv.org/abs/2311.12980
The current body of research on terahertz (THz) wireless communications predominantly focuses on its application for single-user backhaul/fronthaul connectivity at sub-THz frequencies. First, we develop a generalized statistical model for signal prop
Externí odkaz:
http://arxiv.org/abs/2311.06166
Autor:
Leshem, Amir
In this paper, we study the problem of fair multi-agent multi-arm bandit learning when agents do not communicate with each other, except collision information, provided to agents accessing the same arm simultaneously. We provide an algorithm with reg
Externí odkaz:
http://arxiv.org/abs/2306.04498
Autor:
Raviv, Li-on, Leshem, Amir
Cellular networks provide communication for different applications. Some applications have strict and very short latency requirements, while others require high bandwidth with varying priorities. The challenge of satisfying the requirements grows in
Externí odkaz:
http://arxiv.org/abs/2209.15532
Vertical distributed learning exploits the local features collected by multiple learning workers to form a better global model. However, the exchange of data between the workers and the model aggregator for parameter training incurs a heavy communica
Externí odkaz:
http://arxiv.org/abs/2209.01682
Publikováno v:
In Automatica November 2024 169
Publikováno v:
IEEE Trans. on Signal Processing, 2023, pages: 3149-3163
In recent years there is a growing effort to provide learning algorithms for spectrum collaboration. In this paper we present a medium access control protocol which allows spectrum collaboration with minimal regret and high spectral efficiency in hig
Externí odkaz:
http://arxiv.org/abs/2111.12581