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LAARA: Layer-Aware Adaptive Rank Allocation for Parameter-Efficient Fine-Tuning
Ashutosh Tripathi, Surya Deep Singh, Pranab Sahoo +1
Low-Rank Adaptation is widely used for parameter-efficient fine-tuning, yet existing methods typically assign the same adapter rank to every transformer layer despite their heterog…
FedDUAL: A Dual-Strategy with Adaptive Loss and Dynamic Aggregation for Mitigating Data Heterogeneity in Federated Learning
Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha +1
Federated Learning (FL) marks a transformative approach to distributed model training by combining locally optimized models from various clients into a unified global model. While…
FedMRL: Data Heterogeneity Aware Federated Multi-agent Deep Reinforcement Learning for Medical Imaging
Pranab Sahoo, Ashutosh Tripathi, Sriparna Saha +1
Despite recent advancements in federated learning (FL) for medical image diagnosis, addressing data heterogeneity among clients remains a significant challenge for practical implem…
A Comprehensive Survey of Hallucination in Large Language, Image, Video and Audio Foundation Models
Pranab Sahoo, Prabhash Meharia, Akash Ghosh +3
The rapid advancement of foundation models (FMs) across language, image, audio, and video domains has shown remarkable capabilities in diverse tasks. However, the proliferation of…