3 papers
cs.LG2025
Personalized Federated Fine-tuning for Heterogeneous Data: An Automatic Rank Learning Approach via Two-Level LoRA
Jie Hao, Yuman Wu, Ali Payani +2
We study the task of personalized federated fine-tuning with heterogeneous data in the context of language models, where clients collaboratively fine-tune a language model (e.g., B…
cs.LG2024
ProFL: Performative Robust Optimal Federated Learning
Xue Zheng, Tian Xie, Xuwei Tan +2
Performative prediction is a framework that captures distribution shifts that occur during the training of machine learning models due to their deployment. As the trained model is…
cs.DC2024
Enabling Elastic Model Serving with MultiWorld
Myungjin Lee, Akshay Jajoo, Ramana Rao Kompella
Machine learning models have been exponentially growing in terms of their parameter size over the past few years. We are now seeing the rise of trillion-parameter models. The large…