3 papers
cs.LG2025
Parameter-Efficient and Personalized Federated Training of Generative Models at the Edge
Kabir Khan, Manju Sarkar, Anita Kar +1
Large generative models (for example, language and diffusion models) enable high-quality text and image synthesis but are hard to train or adapt in cross-device federated settings…
cs.CL2025
Computational Economics in Large Language Models: Exploring Model Behavior and Incentive Design under Resource Constraints
Sandeep Reddy, Kabir Khan, Rohit Patil +5
Large language models (LLMs) are limited by substantial computational cost. We introduce a "computational economics" framework that treats an LLM as an internal economy of resource…
cs.CL2025
DySK-Attn: A Framework for Efficient, Real-Time Knowledge Updating in Large Language Models via Dynamic Sparse Knowledge Attention
Kabir Khan, Priya Sharma, Arjun Mehta +2
Large Language Models (LLMs) suffer from a critical limitation: their knowledge is static and quickly becomes outdated. Retraining these massive models is computationally prohibiti…