The paper "OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning" addresses the challenge of training large language models (LLMs) on decentralized private data using federated learning (FL). The authors propose OpenFedLLM, a comprehensive framework that integrates federated instruction tuning, federated value alignment, and multiple FL algorithms. The framework supports diverse training datasets and evaluation metrics, enabling a wide range of applications and evaluations. Key contributions of the paper include: 1. **OpenFedLLM Framework**: A concise, integrated, and research-friendly framework that supports federated instruction tuning, federated value alignment, and multiple FL algorithms. 2. **Comprehensive Empirical Study**: Extensive experiments on 7 FL algorithms, 8 training datasets, and over 30 evaluation metrics, demonstrating that FL consistently outperforms local training. 3. **Performance Observations**: FL methods consistently improve model performance, with some algorithms outperforming GPT-4 in specific domains, such as finance. The paper also discusses future directions, including: 1. **Data Management in FedLLM**: Addressing the challenges of data selection and quality in FL. 2. **Heterogeneous Preference in FedVA**: Handling diverse and varying preferences in value alignment. 3. **Personalized Federated Learning**: Developing personalized FL to enhance performance in specific tasks or domains. Overall, the paper provides a robust foundation for collaborative and privacy-preserving LLM training, highlighting the potential of FL in leveraging decentralized private data.The paper "OpenFedLLM: Training Large Language Models on Decentralized Private Data via Federated Learning" addresses the challenge of training large language models (LLMs) on decentralized private data using federated learning (FL). The authors propose OpenFedLLM, a comprehensive framework that integrates federated instruction tuning, federated value alignment, and multiple FL algorithms. The framework supports diverse training datasets and evaluation metrics, enabling a wide range of applications and evaluations. Key contributions of the paper include: 1. **OpenFedLLM Framework**: A concise, integrated, and research-friendly framework that supports federated instruction tuning, federated value alignment, and multiple FL algorithms. 2. **Comprehensive Empirical Study**: Extensive experiments on 7 FL algorithms, 8 training datasets, and over 30 evaluation metrics, demonstrating that FL consistently outperforms local training. 3. **Performance Observations**: FL methods consistently improve model performance, with some algorithms outperforming GPT-4 in specific domains, such as finance. The paper also discusses future directions, including: 1. **Data Management in FedLLM**: Addressing the challenges of data selection and quality in FL. 2. **Heterogeneous Preference in FedVA**: Handling diverse and varying preferences in value alignment. 3. **Personalized Federated Learning**: Developing personalized FL to enhance performance in specific tasks or domains. Overall, the paper provides a robust foundation for collaborative and privacy-preserving LLM training, highlighting the potential of FL in leveraging decentralized private data.