Summary of Efficient Federated Finetuning Of Tiny Transformers with Resource-constrained Devices, by Kilian Pfeiffer et al.
Efficient Federated Finetuning of Tiny Transformers with Resource-Constrained Devices
by Kilian Pfeiffer, Mohamed Aboelenien Ahmed, Ramin Khalili, Jörg Henkel
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC)
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 This paper addresses the challenge of fine-tuning Large Language Models (LLMs) while adhering to resource constraints. LLMs through Transformer structures have dominated text processing tasks but require massive data and high resource requirements. Techniques like Adapter or LoRA have been developed for parameter-efficient fine-tuning, but applying LoRA in federated learning (FL) remains memory- and FLOP-inefficient. The authors propose a novel layer finetuning scheme that enables devices in cross-device FL to utilize pretrained neural networks while respecting resource constraints. Their scheme outperforms the current state of the art under homogeneous or heterogeneous computation and memory constraints, achieving higher accuracies in FL training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make big language models more efficient. These models are great at processing text, but they need a lot of data and computer power to work well. Scientists have developed ways to fine-tune these models without needing so much data or power. However, this approach doesn’t work as well when many devices are working together to learn from each other. The researchers in this paper created a new way for devices to use big language models while respecting the limits of their own resources. This new method does better than current approaches when many devices are involved and is just as good at communicating with each other, making it a step forward for learning. |
Keywords
» Artificial intelligence » Federated learning » Fine tuning » Lora » Parameter efficient » Transformer
