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.
@article{arxiv.2511.16450,
title = {Optimizing Federated Learning in the Era of LLMs: Message Quantization and Streaming},
author = {Ziyue Xu and Zhihong Zhang and Holger R. Roth and Chester Chen and Yan Cheng and Andrew Feng},
journal= {arXiv preprint arXiv:2511.16450},
year = {2025}
}