CG-FedLLM: How to Compress Gradients in Federated Fune-tuning for Large Language Models

TL;DR

This paper proposes CG-FedLLM, a novel gradient compression method for federated fine-tuning of large language models, reducing communication costs while enhancing performance and preserving privacy.

Contribution

It introduces an encoder-decoder framework with new training strategies TGAP and FAF for adaptive gradient compression in federated LLM training.

Findings

Reduces communication costs significantly.

Achieves an average 3-point performance improvement on C-Eval.

Demonstrates robustness and efficiency in privacy-preserving LLM fine-tuning.

Abstract

The success of current Large-Language Models (LLMs) hinges on extensive training data that is collected and stored centrally, called Centralized Learning (CL). However, such a collection manner poses a privacy threat, and one potential solution is Federated Learning (FL), which transfers gradients, not raw data, among clients. Unlike traditional networks, FL for LLMs incurs significant communication costs due to their tremendous parameters. This study introduces an innovative approach to compress gradients to improve communication efficiency during LLM FL, formulating the new FL pipeline named CG-FedLLM. This approach integrates an encoder on the client side to acquire the compressed gradient features and a decoder on the server side to reconstruct the gradients. We also developed a novel training strategy that comprises Temporal-ensemble Gradient-Aware Pre-training (TGAP) to identify characteristic gradients of the target model and Federated AutoEncoder-Involved Fine-tuning (FAF) to compress gradients adaptively. Extensive experiments confirm that our approach reduces communication costs and improves performance (e.g., average 3 points increment compared with traditional CL- and FL-based fine-tuning with LlaMA on a well-recognized benchmark, C-Eval). This improvement is because our encoder-decoder, trained via TGAP and FAF, can filter gradients while selectively preserving critical features. Furthermore, we present a series of experimental analyses focusing on the signal-to-noise ratio, compression rate, and robustness within this privacy-centric framework, providing insight into developing more efficient and secure LLMs.