3 papers
cs.LG2026
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…
cs.LG2026
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…
cs.AI2026
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…