February 10, 2024Open Access

OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning

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Federated learning algorithms outperform local training in enhancing large language models.
In financial benchmarks, models trained with federated learning significantly surpass those trained individually.
Evaluation across 30+ metrics indicates consistent performance gains from collaborative training methods in multiple domains—indicating broad applicability in varied settings and industries by utilizing private data efficiently. The framework supports diverse datasets, enabling robust assessments and ensuring adaptability for various applications.

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Randomized trial shows performance improvement in large language models via federated learning, suggesting a novel approach to data utilization.

Ye et al. (2024) studied this question.

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Randomized trial shows performance improvement in large language models via federated learning, suggesting a novel approach to data utilization.