Originally published at: https://developer.nvidia.com/blog/efficient-federated-learning-in-the-era-of-llms-with-message-quantization-and-streaming/
Federated learning (FL) has emerged as a promising approach for training machine learning models across distributed data sources while preserving data privacy. However, FL faces significant challenges related to communication overhead and local resource constraints when balancing model requirements and communication capabilities. Particularly in the current era of large language models (LLMs), FL faces computational…
Reduce the communication size and reduce the memory usage are very important to Federated training especially for LLM. This work reduced the required memory usage in the LLM streaming and use quantization techniques to reduce the communication message size.