Zobrazeno 1 - 10
of 733
pro vyhledávání: '"GURSOY, M."'
Autor:
Yavuz, A. Dilara, Gursoy, M. Emre
The rapid growth of natural language processing (NLP) and pre-trained language models have enabled accurate text classification in a variety of settings. However, text classification models are susceptible to backdoor attacks, where an attacker embed
Externí odkaz:
http://arxiv.org/abs/2412.18975
In this paper, we propose feature-based federated transfer learning as a novel approach to improve communication efficiency by reducing the uplink payload by multiple orders of magnitude compared to that of existing approaches in federated learning a
Externí odkaz:
http://arxiv.org/abs/2405.09014
Autor:
Wang, Xueyuan, Gursoy, M. Cenk
Unmanned aerial vehicle (UAV)-based networks and Internet of Things (IoT) are being considered as integral components of current and next-generation wireless networks. In particular, UAVs can provide IoT devices with seamless connectivity and high co
Externí odkaz:
http://arxiv.org/abs/2312.06250
Publikováno v:
Volume: 9, Issue: 17, 01 September 2022
Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV non-cooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a chal
Externí odkaz:
http://arxiv.org/abs/2312.06225
Autor:
Wang, Zixi, Gursoy, M. Cenk
Federated learning (FL) is a framework which allows multiple users to jointly train a global machine learning (ML) model by transmitting only model updates under the coordination of a parameter server, while being able to keep their datasets local. O
Externí odkaz:
http://arxiv.org/abs/2312.05761
In this paper, we address the problem of detecting anomalies among a given set of binary processes via learning-based controlled sensing. Each process is parameterized by a binary random variable indicating whether the process is anomalous. To identi
Externí odkaz:
http://arxiv.org/abs/2312.00088
In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations and multiple users. In particular, we propose a novel deep RL framework with multiple act
Externí odkaz:
http://arxiv.org/abs/2311.11206
Federated learning (FL) aims at keeping client data local to preserve privacy. Instead of gathering the data itself, the server only collects aggregated gradient updates from clients. Following the popularity of FL, there has been considerable amount
Externí odkaz:
http://arxiv.org/abs/2310.19222
Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid mult
Externí odkaz:
http://arxiv.org/abs/2210.11272
To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regim
Externí odkaz:
http://arxiv.org/abs/2209.14923