Today, platforms like Hugging Face have made it easier for a wide range of users, from AI researchers to those with limited machine learning experience, to access and utilize pre-trained Large Language Models (LLMs) for different entities. When multiple such organizations or entities share similar tasks of interest but are unable to directly exchange their local data due to privacy regulations, federated learning (FL) emerges as a prominent solution for harnessing the collective data from these entities. FL also provides strong privacy protection, keeps their model ideas safe, and lets them create customized models using different methods.
In this work, researchers have established a comprehensive end-to-end benchmarking pipeline, streamlining the processes of dataset preprocessing, executing or simulating federated fine-tuning, and assessing performance in the context of federated Large Language Model (LLM) fine-tuning, designed for diverse capability demonstration purposes.
The above image demonstrates the architecture of FS-LLM, which consists of three main modules: LLMBENCHMARKS, LLM-ALGZOO, and LLM-TRAINER. The team has developed robust implementations of federated Parameter-Efficient Fine-Tuning (PEFT) algorithms and versatile programming interfaces to facilitate future extensions, enabling LLMs to operate effectively in Federated Learning (FL) scenarios with minimal communication and computation overhead, even when dealing with closed-source LLMs.
A detailed tutorial is provided on their website: federatedscope.io
You can try FederatedScope via FederatedScope Playground or Google Colab.
Their approach also incorporates acceleration techniques and resource-efficient strategies to fine-tune LLMs under resource constraints, along with flexible pluggable sub-routines for interdisciplinary research, such as the application of LLMs in personalized Federated Learning settings.
The research includes a series of extensive and reproducible experiments that validate the effectiveness of FS-LLM and establish benchmarks for advanced LLMs, using state-of-the-art parameter-efficient fine-tuning algorithms within a federated context. . Based on the findings from these experimental results, we outline some promising directions for future research in federated LLM fine-tuning to advance the FL and LLM community.
Check out the Paper and Code. All Credit For This Research Goes To the Researchers on This Project. Also, don’t forget to join our 30k+ ML SubReddit, 40k+ Facebook Community, Discord Channel, and Email Newsletter, where we share the latest AI research news, cool AI projects, and more.
A detailed tutorial is provided on their website: federatedscope.io
You can try FederatedScope via FederatedScope Playground or Google Colab.
If you like our work, you will love our newsletter..
Janhavi Lande, is an Engineering Physics graduate from IIT Guwahati, class of 2023. She is an upcoming data scientist and has been working in the world of ml/ai research for the past two years. She is most fascinated by this ever changing world and its constant demand of humans to keep up with it. In her pastime she enjoys traveling, reading and writing poems.
Credit: Source link
