4 papers
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…
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…
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…
Not All Federated Learning Algorithms Are Created Equal: A Performance Evaluation Study
Gustav A. Baumgart, Jaemin Shin, Ali Payani +2
Federated Learning (FL) emerged as a practical approach to training a model from decentralized data. The proliferation of FL led to the development of numerous FL algorithms and me…