1 citations · 1 across the 2 of their papers we have counts for
3 papers
FedReFT: Federated Representation Fine-Tuning with All-But-Me Aggregation
Fatema Siddika, Md Anwar Hossen, J. Pablo Muñoz +3
Parameter-efficient fine-tuning (PEFT) adapts large pre-trained models by updating only a small subset of parameters. Recently, Representation Fine-Tuning (ReFT) has emerged as an…
FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models
Yan Gao, Massimo Roberto Scamarcia, Javier Fernandez-Marques +18
Large Language Models (LLMs) have achieved state-of-the-art results across diverse domains, yet their development remains reliant on vast amounts of publicly available data, raisin…
Parallel and Distributed Expander Decomposition: Simple, Fast, and Near-Optimal
Daoyuan Chen, Simon Meierhans, Maximilian Probst Gutenberg +1
Expander decompositions have become one of the central frameworks in the design of fast algorithms. For an undirected graph , a near-optimal -expander decomposition is…