Federated Learning: Harnessing Collective Data Safely for Advanced Language Models

Introduction: The Significance of Federated Learning in AI

Accessing and utilizing pre-trained Large Language Models (LLMs) has become easier with platforms like Hugging Face. However, when privacy regulations prevent the direct exchange of local data between organizations or entities, federated learning (FL) offers a solution. Federated learning allows entities to harness collective data while ensuring privacy protection and model idea security. It also enables the creation of customized models using different methods.

The Architecture of FS-LLM

The image above illustrates the architecture of FS-LLM, which comprises three main modules: LLMBENCHMARKS, LLM-ALGZOO, and LLM-TRAINER. These modules facilitate the effective operation of LLMs in Federated Learning (FL) scenarios, even when dealing with closed-source LLMs. The team has also developed robust implementations of federated Parameter-Efficient Fine-Tuning (PEFT) algorithms and versatile programming interfaces to support future extensions.

Features and Future Research

FS-LLM includes acceleration techniques and resource-efficient strategies for fine-tuning LLMs under resource constraints. It also provides flexible pluggable sub-routines for interdisciplinary research, such as personalized Federated Learning settings. The research includes extensive and reproducible experiments that validate the effectiveness of FS-LLM and establish benchmarks for advanced LLMs. These findings pave the way for future research in federated LLM fine-tuning and contribute to the FL and LLM community.

To learn more about FS-LLM, you can visit their website at federatedscope.io. You can also try FederatedScope via the FederatedScope Playground or Google Colab.

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Check out the Paper and Code for more details on this research.

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