4 papers
COSMOS: Model-Agnostic Personalized Federated Learning with Clustered Server Models and Pseudo-Label-Only Communication
Ben Rachmut, Luise Ge, William Yeoh +2
Federated learning (FL) in heterogeneous environments remains challenging because client models often differ in both architecture and data distribution. While recent approaches att…
From Efficiency to Leakage -- Privacy Backdoor in Federated Language Model Fine-Tuning
Shanghao Shi, Chaoyu Zhang, Heng Jin +6
Federated learning (FL) enables multiple parties to collaboratively fine-tune language models for domain-specific tasks without sharing raw data. Since full model fine-tuning is of…
Protecting Language Models Against Unauthorized Distillation through Trace Rewriting
Xinhang Ma, William Yeoh, Ning Zhang +1
Knowledge distillation is a widely adopted technique for transferring capabilities from LLMs to smaller, more efficient student models. However, unauthorized use of knowledge disti…
EcoLoRA: Communication-Efficient Federated Fine-Tuning of Large Language Models
Han Liu, Ruoyao Wen, Srijith Nair +6
To address data locality and privacy restrictions, Federated Learning (FL) has recently been adopted to fine-tune large language models (LLMs), enabling improved performance on var…