Safe-FedLLM: Delving into the Safety of Federated Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on federated learning for large language models (FL) addresses privacy and data-silo issues in the training of large language model training. |
| Approach: | They propose a probe-based defense framework for FedLLM that constructs defenses across three levels: Step-Level, Client-Level and Shadow-Level. |
| Outcome: | The proposed framework improves FedLLM's robustness against malicious clients while maintaining competitive performance on benign data. |
Similar Papers
Can Federated Learning Safeguard Private Data in LLM Training? Vulnerabilities, Attacks, and Defense Evaluation (2025.findings-emnlp)
Copied to clipboard
| Challenge: | federated learning (FL) fine-tunes large language models with local data, but organizations are reluctant to share local data. |
| Approach: | They propose a framework for fine-tuning large language models with local data . they propose centralized fine- tuning with local datasets is a good idea . |
| Outcome: | The proposed framework allows clients to retain local data while sharing only model parameters for training. |
Safely Learning with Private Data: A Federated Learning Framework for Large Language Model (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing large language models (LLMs) use large amounts of public data and massive parameters, but private data is often stored in isolated data silos. |
| Approach: | They propose a Federated Learning framework for large language models which offloads most training parameters to the server while training embedding and output layers locally. |
| Outcome: | The proposed framework achieves comparable metrics to centralized chatGLM model on NLU and generation tasks. |
A Federated Framework for LLM-based Recommendation (2025.findings-naacl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) have demonstrated potential in building generative recommendation systems through fine-tuning user behavior data. |
| Approach: | They propose a federated framework for LLM-based recommendation that combines dynamic parameter aggregation and learning speed for different clients. |
| Outcome: | The proposed framework achieves a more balanced client performance and improved overall performance in a computational and storage-efficient way while safeguarding user privacy well. |
Combating Security and Privacy Issues in the Era of Large Language Models (2024.naacl-tutorials)
Copied to clipboard
| Challenge: | a tutorial aims to provide a summary of risks and vulnerabilities in large language models . a number of studies have focused on security, privacy and copyright aspects of LLMs . |
| Approach: | This tutorial seeks to provide a systematic summary of risks and vulnerabilities in large language models . authors will discuss security, privacy and copyright aspects of LLMs . |
| Outcome: | This tutorial aims to provide a systematic summary of risks and vulnerabilities in large language models . it will also outline emerging challenges in security, privacy and reliability of LLMs . |
FedSpaLLM: Federated Pruning of Large Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing pruning methods assume public access to calibration data, which is impractical for privacy-sensitive applications. |
| Approach: | They propose a federated learning framework for pruning LLMs that prunes models locally based on private data while accounting for system heterogeneity and communication efficiency. |
| Outcome: | The proposed framework reduces communication overhead and personalizes pruning process based on client resources in federated settings. |
SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs (2026.acl-long)
Copied to clipboard
| Challenge: | Existing privacy defenses reduce leakage of PII due to LLM memorization, but often degrade downstream performance. |
| Approach: | They propose a privacy-aware federated fine-tuning framework for large language models that provides fine-grained privacy control without sacrificing utility. |
| Outcome: | The proposed framework reduces PII leakage while providing fine-grained privacy control without sacrificing utility. |
A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)
Copied to clipboard
Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding
| Challenge: | Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded. |
| Approach: | They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy . |
| Outcome: | The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks. |
Promoting Data and Model Privacy in Federated Learning through Quantized LoRA (2024.findings-emnlp)
Copied to clipboard
Zhu JianHao, Changze Lv, Xiaohua Wang, Muling Wu, Wenhao Liu, Tianlong Li, Zixuan Ling, Cenyuan Zhang, Xiaoqing Zheng, Xuanjing Huang
| Challenge: | Existing federated learning frameworks require substantial data and computational resources to develop large language models. |
| Approach: | They propose a method that distributes a quantized version of the model’s parameters during training and combine it with a popular fine-tuning method to significantly reduce communication costs. |
| Outcome: | The proposed method enables accurate estimations for parameter updates while preventing clients from accessing a model whose performance is comparable to the centrally hosted one. |
FedPETuning: When Federated Learning Meets the Parameter-Efficient Tuning Methods of Pre-trained Language Models (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing research on federated learning (FL) for pre-trained language models (PLMs) with increasing concerns about data privacy, enterprises or institutions are not allowed to collect data from end devices or local clients to a centralized server for fine-tuning PLMs. |
| Approach: | They investigate the parameter-efficient tuning of pre-trained language models (PLMs) and develop a federated benchmark for four representative PETuning methods . |
| Outcome: | The proposed method can defend against privacy attacks and maintain acceptable performance with reducing heavy resource consumption. |
Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models (2024.lrec-main)
Copied to clipboard
| Challenge: | Foundation Models (FMs) have demonstrated success in a wide range of applications, but their optimization often requires access to sensitive data. |
| Approach: | They propose a framework that combines FMs and Federated Learning to enable privacy-preserving and collaborative learning across multiple end-users. |
| Outcome: | The proposed framework combines benefits of FMs and Federated Learning (FL) it enables privacy-preserving and collaborative learning across multiple end-users. |