arXiv:2411.02908v2 Announce Type: replace Abstract: Scaling large language models (LLMs) demands extensive data and computing resources, which are traditionally constrained to data centers by the high-bandwidth requirements of distributed training. Low-bandwidth methods like federated learning (FL) could enable collaborative training of larger models across weakly-connected GPUs if they can effectively be used for pre-training. To achieve this, we introduce Photon, the first complete system for federated end-to-end LLM training, leveraging cross-silo FL for global-scale training with minimal c
The increasing computational demands of LLMs are pushing the limits of traditional centralized data centers, creating a need for more distributed and resource-efficient training methodologies.
This development could significantly broaden access to LLM training beyond organizations with massive centralized compute, potentially decentralizing AI development and reducing barriers to entry.
Traditional LLM pre-training, historically reliant on high-bandwidth data centers, can now leverage low-bandwidth federated learning, enabling collaborative training across geographically dispersed and weakly-connected GPUs.
- · GPU manufacturers
- · Organizations with distributed computing resources
- · Researchers with limited access to data centers
- · Edge device manufacturers
- · Cloud providers solely focused on centralized LLM training
- · High-bandwidth data center operators (relatively)
Photon enables federated end-to-end pre-training of large language models, overcoming bandwidth constraints for collaborative training.
This could lead to a proliferation of more diverse and specialized LLMs, trained on decentralized datasets and distributed compute.
The reduced dependency on large centralized data centers might empower smaller entities and nations to develop AI, impacting geopolitical power dynamics in AI development.
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