7 papers
From Task Mixtures to Specialized Experts
Hojat Allah Salehi, Mehrdad Mahdavi, Andrew Arash Mahyari +1
In collaborative foundation model fine-tuning, client data is rarely homogeneous. Instead, clients typically possess unknown mixtures of distinct data distributions, or tasks. Conv…
Don't Overthink, Don't Underthink: Toward Adaptive Reasoning in Agentic AI
Md Jueal Mia, M. Hadi Amini
Recent advances in Large Language Models (LLMs) have shown that increased inference-time reasoning can improve performance on complex tasks. However, many existing approaches rely…
JaiLIP: Jailbreaking Vision-Language Models via Loss Guided Image Perturbation
Md Jueal Mia, M. Hadi Amini
Vision-Language Models (VLMs) have remarkable abilities in generating multimodal reasoning tasks. However, potential misuse or safety alignment concerns of VLMs have increased sign…
An Empirical Analysis of Secure Federated Learning for Autonomous Vehicle Applications
Md Jueal Mia, M. Hadi Amini
Federated Learning lends itself as a promising paradigm in enabling distributed learning for autonomous vehicles applications and ensuring data privacy while enhancing and refining…
FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model
Md Jueal Mia, M. Hadi Amini
Federated Learning (FL) offers a decentralized framework for training and fine-tuning Large Language Models (LLMs) by leveraging computational resources across organizations while…
QuanCrypt-FL: Quantized Homomorphic Encryption with Pruning for Secure Federated Learning
Md Jueal Mia, M. Hadi Amini
Federated Learning has emerged as a leading approach for decentralized machine learning, enabling multiple clients to collaboratively train a shared model without exchanging privat…
CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
Hojat Allah Salehi, Md Jueal Mia, S. Sandeep Pradhan +2
Federated learning (FL) has emerged as a promising framework for distributed machine learning. It enables collaborative learning among multiple clients, utilizing distributed data…