4 papers · 1 filter
Unveiling the Depth-Performance Dilemma in Split-Federated Fine-tuning of LLMs
Hariharan Ramesh, Someshwaran Murugaiyan, Jyotikrishna Dass
Split Federated Fine-tuning (SFF) is a promising paradigm for scaling Large Language Models (LLMs) by partitioning model depth between resource-constrained clients and a centralize…
Spectral Transformation for Layer-wise Global Rank Discovery in Federated LoRA for Vision Transformers
Hariharan Ramesh, Jyotikrishna Dass
Fine-tuning Vision Transformers (ViTs) with low-rank adapters (LoRA) promises better communication efficiency under federated setup, yet existing aggregation strategies face fundam…
GLIDE: Guided Layerwise Hybrid Attention for Efficient LLM Inference
Vimal William, Ravi Tandon, Jyotikrishna Dass
As Large Language Models scale to increasingly long contexts, the memory I/O and computational overhead of the Key-Value (KV) cache during decoding emerges as the primary throughpu…
LLM-MC-Affect: LLM-Based Monte Carlo Modeling of Affective Trajectories and Latent Ambiguity for Interpersonal Dynamic Insight
Yu-Zheng Lin, Bono Po-Jen Shih, John Paul Martin Encinas +7
Emotional coordination is a core property of human interaction that shapes how relational meaning is constructed in real time. While text-based affect inference has become increasi…