Researchers are exploring the challenges of diminishing public data for Large Language Models (LLMs) and proposing collaborative training using federated learning (FL). The OpenFedLLM framework integrates instruction tuning, value alignment, FL algorithms, and datasets for comprehensive exploration. Empirical analyses demonstrate the superiority of FL-fine-tuned LLMs and provide valuable insights for leveraging decentralized data in LLM training.
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Can Machine Learning Evolve Beyond Public Data Limits?
Introducing OpenFedLLM: Pioneering Collaborative and Privacy-Preserving Training of Large Language Models Using Federated Learning
Large Language Models (LLMs), trained on extensive public datasets, have shown remarkable success across various fields. However, the depletion of high-quality public data is imminent by 2026. To address this scarcity, researchers are exploring collaborative and privacy-preserving training methods for LLMs.
One practical solution is OpenFedLLM, developed by researchers from Shanghai Jiao Tong University, Zhejiang University, and Shanghai AI Laboratory. This framework facilitates collaborative and privacy-preserving training of LLMs on distributed private data through federated learning (FL). Empirical studies demonstrate FL’s superiority over individual training, especially in resource-constrained scenarios, with potential applications in finance.
The OpenFedLLM framework focuses on training LLMs via FL while preserving privacy. It integrates federated instruction tuning, value alignment, and diverse FL algorithms, offering a user-friendly interface for both LLM and FL communities. The framework follows standard FL protocols, enabling seamless integration with various FL algorithms and facilitating collaborative model training across distributed parties.
Researchers have outlined a holistic approach to training LLMs using FL on distributed private data, offering a promising avenue amid diminishing public data. The work contributes valuable insights and methodologies for leveraging decentralized data in LLM training.
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