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Menampilkan 1–6 dari 6 artikel
Efficient TinyML Architectures for On-Device Small Language Models: Privacy-Preserving Inference at the Edge
Mangesh Pujari
; Anshul Goel
; Anil Kumar Pakina
International Journal Science and Technology (IJST)
Vol 3
, No 3
(2024)
Deploying small language models (SLMs) on ultra-low-power edge devices requires careful optimization to meet strict memory, latency, and energy constraints while preserving privacy. This paper presents a systematic approach to adapting SLMs for Tiny ML, focusing on model compression, hardware-aware quantization, and lightweight privacy mechanisms. We introduce a sparse ternary quantization technique that reduces model size by 5.8× with minimal accuracy loss and an efficient federated fine-tuning...
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Ensuring Responsible AI: The Role of Supervised Fine-Tuning (SFT) in Upholding Integrity and Privacy Regulations
Tejaskumar Pujari
; Anshul Goel
; Ashwin Sharma
International Journal Science and Technology (IJST)
Vol 3
, No 3
(2024)
AI is increasingly used in high-stakes fields such as healthcare, finance, education, and public governance, requiring systems that uphold fairness, accountability, transparency, and privacy. This paper highlights the critical role of Supervised Fine-Tuning (SFT) in aligning large AI models with ethical principles and regulatory frameworks like the GDPR and EU AI Act.
The interdisciplinary approach combines regulatory analysis, technical research, and case studies. It proposes integrating privac...
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Ethical and Responsible AI: Governance Frameworks and Policy Implications for Multi-Agent Systems
Tejaskumar Pujari
; Anshul Goel
; Ashwin Sharma
International Journal Science and Technology (IJST)
Vol 3
, No 1
(2024)
Semi-autonomous, augmented- Artificial Intelligence has become increasingly relevant as collective activities are practiced by two or more autonomic entities. MAS and AI at the intersection have fostered very new waves of socioeconomic exchange, necessitating technological governance and, the most challenging element of them all, ethical governance. These autonomous systems involve a network of decision-making agents working in a decentralized environment, entailing very high accountability, tra...
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Balancing Innovation and Privacy: A Red Teaming Approach to Evaluating Phone-Based Large Language Models under AI Privacy Regulations
Mangesh Pujari
; Anil Kumar Pakina
; Anshul Goel
International Journal Science and Technology (IJST)
Vol 2
, No 3
(2023)
The rapid deployment of large language models (LLMs) on mobile devices has introduced significant privacy concerns, particularly regarding data collection, user profiling, and compliance with evolving AI regulations such as the GDPR and the AI Act. While these on-device LLMs promise improved latency and user experience, their potential to inadvertently leak sensitive information remains understudied. This paper proposes a red teaming framework to systematically assess the privacy risks of phone-...
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Ethical and Responsible AI in the Age of Adversarial Diffusion Models: Challenges, Risks, and Mitigation Strategies
Tejaskumar Pujari
; Anshul Goel
; Deepak Kejriwal
International Journal Science and Technology (IJST)
Vol 1
, No 3
(2022)
The rapid pace of diffusion models in generative AI has completely restructured many fields, particularly with respect to image synthesis, video generation, and creative data enhancement. However, promising developments remain tinged with ethical questions in view of diffusion-based model dual-use. By misusing these models, purveyors could think up deepfaked videos, unpredictable forms of misinformation, instead outing cyber warfare-related attacks over the Internet, therefore aggravating societ...
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Enhancing Cybersecurity in Edge AI through Model Distillation and Quantization: A Robust and Efficient Approach
Mangesh Pujari
; Anshul Goel
; Ashwin Sharma
International Journal Science and Technology (IJST)
Vol 1
, No 3
(2022)
The rapid proliferation of Edge AI has introduced significant cybersecurity challenges, including adversarial attacks, model theft, and data privacy concerns. Traditional deep learning models deployed on edge devices often suffer from high computational complexity and memory requirements, making them vulnerable to exploitation. This paper explores the integration of model distillation and quantization techniques to enhance the security and efficiency of Edge AI systems. Model distillation reduce...
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