Summary of Sa-fedlora: Adaptive Parameter Allocation For Efficient Federated Learning with Lora Tuning, by Yuning Yang et al.
SA-FedLora: Adaptive Parameter Allocation for Efficient Federated Learning with LoRA Tuning
by Yuning Yang, Xiaohong Liu, Tianrun Gao, Xiaodong Xu, Guangyu Wang
First submitted to arxiv on: 15 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Distributed, Parallel, and Cluster Computing (cs.DC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a novel approach to federated learning (FL) called Simulated Annealing-based Federated Learning with LoRA tuning (SA-FedLoRA). This method aims to reduce the trainable parameters in FL networks, which is crucial for large-scale pre-trained models due to high communication costs. The authors suggest that parameter-efficient fine-tuning methods like Low-Rank Adaptation (LoRA) can be effective but may lead to overfitting or slower convergence. To address this challenge, SA-FedLoRA consists of two stages: initiating and annealing. In the initiating stage, a parameter regularization approach is implemented to mitigate client drift and accelerate convergence. In the annealing stage, the algorithm allocates higher parameter budgets during the early ‘heating’ phase and then gradually reduces them until the ‘cooling’ phase. The authors demonstrate that SA-FedLoRA achieves superior performance compared to FedAvg while significantly reducing communication parameters by up to 93.62%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores a new way to train models on local data without sharing it, using something called federated learning (FL). FL helps keep sensitive information safe while still training models that can be used for many different tasks. However, this method requires a lot of communication between devices, which takes up a lot of resources. To make things more efficient, the authors propose an approach that reduces the amount of data sent back and forth. They use something called Simulated Annealing-based Federated Learning with LoRA tuning (SA-FedLoRA) to achieve this. The method works by gradually adjusting how much data is sent during training. This way, SA-FedLoRA can train models more efficiently while still keeping sensitive information safe. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Low rank adaptation » Overfitting » Parameter efficient » Regularization
