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

arXiv: https://arxiv.org/abs/2402.06954 · HTML: https://mlsanity.com/paper/2402.06954/

## Key facts

- Cited by 128 papers in the map (data through 14 Oct 2026).
- Builds on 105 of its 145 references that are in the map.
- First posted on arXiv 10 Feb 2024.

## Builds on (most cited first, 30 of 105)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Training Verifiers to Solve Math Word Problems](https://mlsanity.com/paper/2110.14168/) (2021)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Large Language Models are Zero-Shot Reasoners](https://mlsanity.com/paper/2205.11916/) (2022)
- [Prefix-Tuning: Optimizing Continuous Prompts for Generation](https://mlsanity.com/paper/2101.00190/) (2021)
- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback](https://mlsanity.com/paper/2204.05862/) (2022)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [QLoRA: Efficient Finetuning of Quantized LLMs](https://mlsanity.com/paper/2305.14314/) (2023)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://mlsanity.com/paper/2307.15043/) (2023)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)

## Built on by (most cited first, 30 of 128)

- [FLoRA: Federated Fine-Tuning Large Language Models with Heterogeneous Low-Rank Adaptations](https://mlsanity.com/paper/2409.05976/) (2024)
- [SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2407.00952/) (2024)
- [Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models](https://mlsanity.com/paper/2407.06089/) (2024)
- [Advances and Open Challenges in Federated Foundation Models](https://mlsanity.com/paper/2404.15381/) (2024)
- [FedLLM-Bench: Realistic Benchmarks for Federated Learning of Large Language Models](https://mlsanity.com/paper/2406.04845/) (2024)
- [When One LLM Drools, Multi-LLM Collaboration Rules](https://mlsanity.com/paper/2502.04506/) (2025)
- [Federated Large Language Models: Current Progress and Future Directions](https://mlsanity.com/paper/2409.15723/) (2024)
- [Time-FFM: Towards LM-Empowered Federated Foundation Model for Time Series Forecasting](https://mlsanity.com/paper/2405.14252/) (2024)
- [Emerging Safety Attack and Defense in Federated Instruction Tuning of Large Language Models](https://mlsanity.com/paper/2406.10630/) (2024)
- [Keeping Yourself is Important in Downstream Tuning Multimodal Large Language Model](https://mlsanity.com/paper/2503.04543/) (2025)
- [Can Textual Gradient Work in Federated Learning?](https://mlsanity.com/paper/2502.19980/) (2025)
- [Synergizing Foundation Models and Federated Learning: A Survey](https://mlsanity.com/paper/2406.12844/) (2024)
- [Towards Federated RLHF with Aggregated Client Preference for LLMs](https://mlsanity.com/paper/2407.03038/) (2024)
- [Open Challenges and Opportunities in Federated Foundation Models Towards Biomedical Healthcare](https://mlsanity.com/paper/2405.06784/) (2024)
- [Federated Continual Instruction Tuning](https://mlsanity.com/paper/2503.12897/) (2025)
- [KnowledgeSG: Privacy-Preserving Synthetic Text Generation with Knowledge Distillation from Server](https://mlsanity.com/paper/2410.05725/) (2024)
- [FlowerTune: A Cross-Domain Benchmark for Federated Fine-Tuning of Large Language Models](https://mlsanity.com/paper/2506.02961/) (2025)
- [RFLPA: A Robust Federated Learning Framework against Poisoning Attacks with Secure Aggregation](https://mlsanity.com/paper/2405.15182/) (2024)
- [MobileA3gent: Training Mobile GUI Agents Using Decentralized Self-Sourced Data from Diverse Users](https://mlsanity.com/paper/2502.02982/) (2025)
- [Federated Learning for Cross-Domain Data Privacy: A Distributed Approach to Secure Collaboration](https://mlsanity.com/paper/2504.00282/) (2025)
- [pfl-research: simulation framework for accelerating research in Private Federated Learning](https://mlsanity.com/paper/2404.06430/) (2024)
- [Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model](https://mlsanity.com/paper/2409.19390/) (2024)
- [Federated In-Context Learning: Iterative Refinement for Improved Answer Quality](https://mlsanity.com/paper/2506.07440/) (2025)
- [Large Language Models meet Network Slicing Management and Orchestration](https://mlsanity.com/paper/2403.13721/) (2024)
- [Thinking Forward: Memory-Efficient Federated Finetuning of Language Models](https://mlsanity.com/paper/2405.15551/) (2024)
- [FedMLLM: Federated Fine-tuning MLLM on Multimodal Heterogeneity Data](https://mlsanity.com/paper/2411.14717/) (2024)
- [Mitigating Unintended Memorization with LoRA in Federated Learning for LLMs](https://mlsanity.com/paper/2502.05087/) (2025)
- [Communication-Efficient Federated Low-Rank Update Algorithm and its Connection to Implicit Regularization](https://mlsanity.com/paper/2409.12371/) (2024)
- [One-shot Federated Learning Methods: A Practical Guide](https://mlsanity.com/paper/2502.09104/) (2025)
- [LLMs meet Federated Learning for Scalable and Secure IoT Management](https://mlsanity.com/paper/2504.16032/) (2025)

## Abstract (arXiv)

Trained on massive publicly available data, large language models (LLMs) have demonstrated tremendous success across various fields. While more data contributes to better performance, a disconcerting reality is that high-quality public data will be exhausted in a few years. In this paper, we offer a potential next step for contemporary LLMs: collaborative and privacy-preserving LLM training on the underutilized distributed private data via federated learning (FL), where multiple data owners collaboratively train a shared model without transmitting raw data. To achieve this, we build a concise, integrated, and research-friendly framework/codebase, named OpenFedLLM. It covers federated instruction tuning for enhancing instruction-following capability, federated value alignment for aligning with human values, and 7 representative FL algorithms. Besides, OpenFedLLM supports training on diverse domains, where we cover 8 training datasets; and provides comprehensive evaluations, where we cover 30+ evaluation metrics. Through extensive experiments, we observe that all FL algorithms outperform local training on training LLMs, demonstrating a clear performance improvement across a variety of settings. Notably, in a financial benchmark, Llama2-7B fine-tuned by applying any FL algorithm can outperform GPT-4 by a significant margin while the model obtained through individual training cannot, demonstrating strong motivation for clients to participate in FL. The code is available at https://github.com/rui-ye/OpenFedLLM.