The Future of Large Language Model Pre-training is Federated (2405.10853v3)

Abstract: Generative pre-trained LLMs have demonstrated impressive performance over a wide range of tasks, thanks to the unprecedented amount of data they have been trained on. As established scaling laws indicate, LLMs' future performance improvement depends on the amount of computing and data sources they can leverage for pre-training. Federated learning (FL) has the potential to unleash the majority of the planet's data and computational resources, which are underutilized by the data-center-focused training methodology of current LLM practice. Our work presents a robust, flexible, reproducible FL approach that enables large-scale collaboration across institutions to train LLMs. We propose a scalable deployment system called Photon to enable the investigation and development of this new training paradigm for LLM pre-training. We show that Photon can be used by organizations interested in collaborating with their private data sources and computational resources for pre-training LLMs with billions of parameters. This paradigm would mobilize more computational and data resources while matching or potentially exceeding centralized performance. We further show the effectiveness of the federated training scales with model size and present our approach for training billion-scale federated LLMs using limited resources. Thus far, we have used Photon to train LLM models to the size of 7B parameters and anticipate larger models being completed in the near future. Finally, we show that LLM training is highly resilient to the classical challenges of federated statistical and hardware heterogeneity. Furthermore, we show that convergence is robust to partial participation, opening the avenue for compute-efficient collaborative training. Photon will help data-rich actors to become the protagonists of LLMs pre-training instead of leaving the stage to compute-rich actors alone.

• The paper introduces a federated approach that matches centralized training performance while enabling collaborative LLM pre-training.
• It details a scalable methodology by training up to 1.3 billion parameters using local SGD to reduce communication overhead.
• The framework democratizes LLM access by leveraging global data and diverse hardware, ensuring privacy and resource efficiency.

The Future of LLM Pre-training is Federated

The paper "The Future of LLM Pre-training is Federated" presents a pivotal shift in the paradigm of training LLMs by leveraging federated learning (FL). The authors propose that the most effective means of improving LLM performance is to move away from the current centralized, compute-intensive model training methodology and adopt a federated, collaborative approach. This shift aims to democratize access to LLM training by harnessing the underutilized data and computational resources distributed globally.

Core Contributions

The principal contributions of the paper are the development of a robust, flexible, and reproducible federated learning framework that can facilitate the training of LLMs on a global scale:

1. Federated Learning for LLMs: The authors present a federated approach to training LLMs, enabling collaborative utilization of data and computational resources across various institutions. This method not only matches but potentially exceeds the performance of centralized training methodologies.
2. Scalability: The paper documents the successful training of LLMs of up to 1.3 billion parameters using the federated approach. This is the first recorded instance of generative pre-training of a billion-scale model within a heterogeneous federated setting.
3. Communication Efficiency: The federated learning strategy significantly reduces communication overhead compared to traditional centralized methods, making it feasible for institutions with limited computing power and less powerful network infrastructures to participate.
4. Broad Hardware Inclusivity: The technique accommodates participants with diverse hardware capabilities, ranging from powerful GPUs to standard cloud-based setups with single GPUs.
5. Empirical Validation: Extensive experiments validate the model's efficacy and performance, demonstrating that larger federated models reach consensus more easily and efficiently compared to smaller ones.

Methodological Insights

The research explores several key dimensions of federated training:

• Data and Model Parallelism: The federated approach leverages both data and model parallelism to distribute the training load across multiple nodes. This distribution reduces the memory load on individual GPUs and aligns with the scalability goals.
• Local SGD and Gradients: By employing local stochastic gradient descent (SGD), the federated framework mitigates the need for synchronous updates and reduces the communication burden. The results showed that federating the optimization process helps align client models towards a global optimum more effectively.
• Memory and Computation Management: Techniques such as activation checkpointing and CPU offloading are employed to manage the memory and computational requirements of the training process, making it accessible to a wider range of hardware configurations.

Experimental Validation

The authors conducted rigorous experiments using a variant of the C4 dataset, randomly split across eight clients. Key findings include:

• Larger Models Achieve Better Consensus: The research indicates that federated optimization improves convergence and performance as the model size increases. For example, the convergence phase for a 1.3 billion parameter model occurs much more rapidly compared to a smaller 75 million parameter model.
• Performance Comparisons: When comparing the federated approach to centralized training, larger federated models demonstrated performance parity with centralized models, proving the feasibility and effectiveness of federated learning for large-scale LLMs.

Implications and Future Research

The implications of this research are profound:

• Democratization of LLM Training: By enabling entities with significant data but limited computational resources to participate in LLM training, the federated approach democratizes access to high-quality LLMs.
• Privacy-Preserving Data Utilization: FL inherently supports privacy-preserving techniques, making it possible to utilize sensitive data without compromising privacy.
• Scalability and Data Sources: The ability to aggregate diverse data sources can enhance model generalization and reduce biases inherent in models trained on limited data sources.

Future research directions proposed in the paper include further optimizing the federated training framework, scaling up both the population of clients and the size of the models, and exploring the impact of data heterogeneity on model performance. Moreover, fine-tuning the federated models on established benchmark tasks will provide deeper insights into their utility across a broad range of applications.

Conclusion

This paper advances the field of LLM pre-training by introducing and validating a federated learning framework that democratizes access to model training capabilities. This framework leverages the untapped data and computational resources distributed worldwide, proving that a collaborative approach can match and potentially surpass the performance of centralized methodologies. The authors' rigorous empirical validation and thoughtful consideration of future work make a compelling case for federated learning as a sustainable and inclusive path forward in AI development.