Summary of Sequential Compression Layers For Efficient Federated Learning in Foundational Models, by Navyansh Mahla et al.
Sequential Compression Layers for Efficient Federated Learning in Foundational Models
by Navyansh Mahla, Sunny Gupta, Amit Sethi
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated Learning (FL) enables fine-tuning of large language models (LLMs) on private data across multiple nodes. While LoRA has been widely used for parameter-efficient federated fine-tuning, recent studies indicate its suboptimal performance in the FL context. This paper proposes a novel, simple, and effective parameter-efficient fine-tuning method that doesn’t rely on LoRA. The approach introduces a small multi-layer perceptron (MLP) layer between existing MLP layers within the transformer block, addressing bottlenecks associated with LoRA in federated fine-tuning. Experimental results demonstrate superior performance for both language models and vision encoders compared to recent LoRA-based approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to train big language models on lots of private data without sharing the data itself. This is called Federated Learning (FL). Right now, there’s a popular method called LoRA that helps with this process, but it has some limitations. The authors of this paper came up with a new way to fine-tune these models that doesn’t use LoRA and actually works better. They added a small extra layer to the model that helps improve its performance when training on private data. This approach worked well for both language-based tasks and visual tasks, showing it’s a useful tool for researchers and developers. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Parameter efficient » Transformer
