Summary of Fed-pilot: Optimizing Lora Assignment For Efficient Federated Foundation Model Fine-tuning, by Zikai Zhang et al.
Fed-piLot: Optimizing LoRA Assignment for Efficient Federated Foundation Model Fine-Tuning
by Zikai Zhang, Jiahao Xu, Ping Liu, Rui Hu
First submitted to arxiv on: 14 Oct 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 A new paper proposes an efficient framework for fine-tuning foundation models in federated learning settings, addressing challenges posed by clients with heterogeneous resources. Specifically, Federated FMs (FedFMs) fine-tune using low-rank adaptation (LoRA) modules to balance parameter efficiency and data privacy. To tackle the issue of varying GPU memory capacity among clients, the paper introduces Fed-piLot, a framework that optimizes local LoRA assignments for heterogeneous clients by solving a Knapsack Optimization Problem. The framework uses Local-Global Information Gain Score (IG-Score) as its value function to optimize LoRA assignment under clients’ memory constraints and mitigates heterogeneity in model updates using a novel Spatial-Temporal model aggregation (STAgg) rule with Dynamic Weight Adjustment (DWA). Experimental results on three datasets demonstrate the effectiveness and efficiency of Fed-piLot. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way for computers to work together without sharing their data. Foundation models are special kinds of AI that can learn from many different sources. This paper makes a new framework, called Fed-piLot, that helps these foundation models learn better when working with other computers that have different amounts of memory and processing power. The framework uses a special way of assigning tasks to each computer based on its available resources. It also has a way to combine the results from each computer to get a final answer. The paper tested this framework on three sets of data and showed that it works well. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Low rank adaptation » Optimization » Temporal model
