Zobrazeno 1 - 10
of 412
pro vyhledávání: '"Al-Fuqaha, Ala"'
We propose clustered federated multitask learning to address statistical challenges in non-independent and identically distributed data across clients. Our approach tackles complexities in hierarchical wireless networks by clustering clients based on
Externí odkaz:
http://arxiv.org/abs/2407.09219
The increasing popularity of electric vehicles (EVs) necessitates robust defenses against sophisticated cyber threats. A significant challenge arises when EVs intentionally provide false information to gain higher charging priority, potentially causi
Externí odkaz:
http://arxiv.org/abs/2407.03729
Recently, Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users in diverse scenarios like festivals, large sporting events, natural and man-made disasters
Externí odkaz:
http://arxiv.org/abs/2406.16934
In the past decade, Unmanned Aerial Vehicles (UAVs) have grabbed the attention of researchers in academia and industry for their potential use in critical emergency applications, such as providing wireless services to ground users and collecting data
Externí odkaz:
http://arxiv.org/abs/2401.11118
Clustered Federated Multitask Learning (CFL) has gained considerable attention as an effective strategy for overcoming statistical challenges, particularly when dealing with non independent and identically distributed (non IID) data across multiple u
Externí odkaz:
http://arxiv.org/abs/2401.10646
Autor:
Al-Maliki, Shawqi, Qayyum, Adnan, Ali, Hassan, Abdallah, Mohamed, Qadir, Junaid, Hoang, Dinh Thai, Niyato, Dusit, Al-Fuqaha, Ala
Deep Neural Networks (DNNs) have been the driving force behind many of the recent advances in machine learning. However, research has shown that DNNs are vulnerable to adversarial examples -- input samples that have been perturbed to force DNN-based
Externí odkaz:
http://arxiv.org/abs/2310.03614
Autor:
Butt, Muhammad Atif, Ali, Hassan, Qayyum, Adnan, Sultani, Waqas, Al-Fuqaha, Ala, Qadir, Junaid
Semantic understanding of roadways is a key enabling factor for safe autonomous driving. However, existing autonomous driving datasets provide well-structured urban roads while ignoring unstructured roadways containing distress, potholes, water puddl
Externí odkaz:
http://arxiv.org/abs/2308.06393
Several membership inference (MI) attacks have been proposed to audit a target DNN. Given a set of subjects, MI attacks tell which subjects the target DNN has seen during training. This work focuses on the post-training MI attacks emphasizing high co
Externí odkaz:
http://arxiv.org/abs/2307.05193
Autor:
Aledhari, Mohammed, Rahouti, Mohamed, Qadir, Junaid, Qolomany, Basheer, Guizani, Mohsen, Al-Fuqaha, Ala
This article outlines the architecture of autonomous driving and related complementary frameworks from the perspective of human comfort. The technical elements for measuring Autonomous Vehicle (AV) user comfort and psychoanalysis are listed here. At
Externí odkaz:
http://arxiv.org/abs/2306.09462
Autor:
Albaseer, Abdullatif, Abdallah, Mohamed, Al-Fuqaha, Ala, Mohammed, Abegaz, Erbad, Aiman, Dobre, Octavia A.
Clustered federated Multitask learning is introduced as an efficient technique when data is unbalanced and distributed amongst clients in a non-independent and identically distributed manner. While a similarity metric can provide client groups with s
Externí odkaz:
http://arxiv.org/abs/2304.13423