Optimizing Federated Learning in the Era of LLMs: Message Quantization and Streaming

TL;DR

This paper presents techniques like message quantization and streaming to improve communication efficiency and scalability in federated learning with large language models, addressing key resource constraints.

Contribution

It introduces advanced message quantization and streaming methods to optimize federated learning workflows for large language models, enhancing scalability and efficiency.

Findings

Reduced communication overhead through quantization

Improved memory management with streaming techniques

Enhanced robustness and scalability in FL with LLMs

Abstract

Federated Learning (FL) offers a promising solution for training machine learning models across distributed data sources while preserving data privacy. However, FL faces critical challenges related to communication overhead and local resource constraints, especially in the era of Large Language Models (LLMs) with billions of parameters. The sheer size of these models exacerbates both memory and communication constraints, making efficient transmission and processing essential for practical deployment. NVIDIA FLARE, an open-source SDK for federated learning, addresses these challenges by introducing advanced communication capabilities. Building upon existing solutions for large object streaming, we enhance FL workflows for LLMs through two key techniques: message quantization and container/file streaming. Quantization reduces message size, while streaming enables efficient memory management, improving scalability and integration with existing workflows. These advancements significantly enhance the robustness and efficiency of FL with LLMs, ensuring better performance in real-world federated learning scenarios.