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FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

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arxiv 2310.10049 v1 pith:WGBUCCVU submitted 2023-10-16 cs.LG cs.AI

Tao Fan , Yan Kang , Guoqiang Ma , Weijing Chen , Wenbin Wei , Lixin Fan , Qiang Yang This is my paper
classification cs.LGcs.AI
keywords fate-llmlargellmslanguagemodelstrainingfederatedfedllm
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Language Models (LLMs), such as ChatGPT, LLaMA, GLM, and PaLM, have exhibited remarkable performances across various tasks in recent years. However, LLMs face two main challenges in real-world applications. One challenge is that training LLMs consumes vast computing resources, preventing LLMs from being adopted by small and medium-sized enterprises with limited computing resources. Another is that training LLM requires a large amount of high-quality data, which are often scattered among enterprises. To address these challenges, we propose FATE-LLM, an industrial-grade federated learning framework for large language models. FATE-LLM (1) facilitates federated learning for large language models (coined FedLLM); (2) promotes efficient training of FedLLM using parameter-efficient fine-tuning methods; (3) protects the intellectual property of LLMs; (4) preserves data privacy during training and inference through privacy-preserving mechanisms. We release the code of FATE-LLM at https://github.com/FederatedAI/FATE-LLM to facilitate the research of FedLLM and enable a broad range of industrial applications.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 32 citations worldwide. Full citation record

  1. Federated Foundation Models Fine-Tuning with Heterogeneous Compressed Clients

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    A federated fine-tuning framework that compresses foundation models on clients via SVD, aggregates adapters within groups and full-rank reconstructions across groups, then distills the result back into the full server model.

  2. HermesHFL: Incentive-Compatible Hierarchical Federated Unlearning for Dynamic LLM Fine-Tuning

    cs.CE 2026-07 reject novelty 6.0 of 10

    HermesHFL plus Neogen jointly optimize incentives, client–edge association, and gradient-ascent unlearning so hierarchical LoRA fine-tuning remains useful after clients leave and rejoin.

  3. LLM in the Middle: A Systematic Review of Threats and Mitigations to Real-World LLM-based Systems

    cs.CR 2025-09 conditional novelty 6.0 of 10

    A systematic review that categorizes LLM threats, severity scores, and mitigations across development and operation life cycles and multiple deployment scenarios.

  4. SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation

    cs.CR 2025-06 conditional novelty 3.0 of 10

    A systematization-of-knowledge survey that categorizes LLM privacy risks into training data, prompts, outputs, and agents, and reviews limitations of current mitigations.

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