Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Adhikary, Apurba"'
In this study, we explore the efficacy of advanced pre-trained architectures, such as Vision Transformers (ViT), ConvNeXt, and Swin Transformers in enhancing Federated Domain Generalization. These architectures capture global contextual features and
Externí odkaz:
http://arxiv.org/abs/2409.13527
Advancements in 6G wireless technology have elevated the importance of beamforming, especially for attaining ultra-high data rates via millimeter-wave (mmWave) frequency deployment. Although promising, mmWave bands require substantial beam training t
Externí odkaz:
http://arxiv.org/abs/2406.02000
Federated learning (FL) facilitates a privacy-preserving neural network training paradigm through collaboration between edge clients and a central server. One significant challenge is that the distributed data is not independently and identically dis
Externí odkaz:
http://arxiv.org/abs/2404.09259
Federated learning (FL) is a privacy-preserving distributed framework for collaborative model training on devices in edge networks. However, challenges arise due to vulnerability to adversarial examples (AEs) and the non-independent and identically d
Externí odkaz:
http://arxiv.org/abs/2404.06776
Federated learning (FL) is a privacy-preserving distributed management framework based on collaborative model training of distributed devices in edge networks. However, recent studies have shown that FL is vulnerable to adversarial examples (AEs), le
Externí odkaz:
http://arxiv.org/abs/2403.02803
This work designs a novel semantic communication (SemCom) framework for the next-generation wireless network to tackle the challenges of unnecessary transmission of vast amounts that cause high bandwidth consumption, more latency, and experience with
Externí odkaz:
http://arxiv.org/abs/2310.09021
Autor:
Qiao, Yu, Munir, Md. Shirajum, Adhikary, Apurba, Le, Huy Q., Raha, Avi Deb, Zhang, Chaoning, Hong, Choong Seon
Federated learning-assisted edge intelligence enables privacy protection in modern intelligent services. However, not independent and identically distributed (non-IID) distribution among edge clients can impair the local model performance. The existi
Externí odkaz:
http://arxiv.org/abs/2304.01950
Autor:
Prottasha, Nusrat Jahan, Murad, Saydul Akbar, Muzahid, Abu Jafar Md, Rana, Masud, Kowsher, Md, Adhikary, Apurba, Biswas, Sujit, Bairagi, Anupam Kumar
Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explici
Externí odkaz:
http://arxiv.org/abs/2211.02263
Autor:
Munir, Md. Shirajum, Kim, Ki Tae, Adhikary, Apurba, Saad, Walid, Shetty, Sachin, Park, Seong-Bae, Hong, Choong Seon
Explainable artificial intelligence (XAI) twin systems will be a fundamental enabler of zero-touch network and service management (ZSM) for sixth-generation (6G) wireless networks. A reliable XAI twin system for ZSM requires two composites: an extrem
Externí odkaz:
http://arxiv.org/abs/2210.06649
Publikováno v:
In Information Fusion January 2025 113
