FedShield-LLM: A Secure and Scalable Federated Fine-Tuned Large Language Model (2506.05640v1)

Abstract: Federated Learning (FL) offers a decentralized framework for training and fine-tuning LLMs by leveraging computational resources across organizations while keeping sensitive data on local devices. It addresses privacy and security concerns while navigating challenges associated with the substantial computational demands of LLMs, which can be prohibitive for small and medium-sized organizations. FL supports the development of task-specific LLMs for cross-silo applications through fine-tuning but remains vulnerable to inference attacks, such as membership inference and gradient inversion, which threaten data privacy. Prior studies have utilized Differential Privacy (DP) in LLM fine-tuning, which, despite being effective at preserving privacy, can degrade model performance. To overcome these challenges, we propose a novel method, FedShield-LLM, that uses pruning with Fully Homomorphic Encryption (FHE) for Low-Rank Adaptation (LoRA) parameters, enabling secure computations on encrypted model updates while mitigating the attack surface by deactivating less important LoRA parameters. Furthermore, optimized federated algorithms for cross-silo environments enhance scalability and efficiency. Parameter-efficient fine-tuning techniques like LoRA substantially reduce computational and communication overhead, making FL feasible for resource-constrained clients. Experimental results show that the proposed method outperforms existing methods while maintaining robust privacy protection, enabling organizations to collaboratively train secure and efficient LLMs. The code and data are available at, https://github.com/solidlabnetwork/fedshield-LLM

Overview of FedShield-LLM: A Federated Fine-Tuning Approach for Secure and Scalable LLMs

The paper "FedShield-LLM: A Secure and Scalable Federated Fine-Tuned LLM" presents an innovative approach to federated learning (FL) aimed at enhancing the security and scalability of LLMs. Federation in machine learning offers a decentralized way of training models by leveraging computational resources across organizations, which inherently supports data privacy by keeping sensitive information localized. However, federated learning, particularly for LLMs, requires addressing computational demands and security vulnerabilities, including inference attacks such as membership inference and gradient inversion. This paper introduces FedShield-LLM, a method that employs pruning combined with Fully Homomorphic Encryption (FHE) to safeguard model updates during the fine-tuning process.

Methodology and Innovations

FedShield-LLM proposes using Low-Rank Adaptation (LoRA) parameters to leverage computational efficiency, coupled with sophisticated encryption techniques to mitigate privacy risks. Here's a brief overview of the major components:

1. Pruning Technique: By deactivating less critical LoRA parameters, this method reduces the attack surface of the model, thereby improving security without excessively compromising performance.
2. Fully Homomorphic Encryption: FHE allows computations over encrypted data, ensuring that the model updates remain confidential and secure from inference attacks throughout the federated learning process.
3. LoRA Parameters: The implementation of LoRA allows the fine-tuning process to focus on specific layers, reducing computational overhead substantially compared to full-weight updates, which makes the approach feasible for small and medium-sized enterprises.

Experimental Validation

The experimental section of the paper validates the approach using the meta-llama/Llama-2 models across diversified datasets, including those for medical, financial, and mathematical applications. Metrics such as training loss and BERT score comparisons indicate that FedShield-LLM achieves competitive performance in text generation tasks while offering enhanced security. Notably, the framework effectively balances privacy with accuracy, outstripping Differential Privacy mechanisms that typically degrade model performance.

Implications and Future Directions

FedShield-LLM represents a significant step toward enhancing privacy in federated learning environments for LLMs. The integration of homomorphic encryption with LoRA and pruning offers a novel solution to reduce both computational complexity and data vulnerability. Practically, this framework enables secure collaboration across sectors like healthcare and finance, where data sensitivity is paramount. Moreover, theoretically, it opens a pathway for more reliable federated learning deployments amidst growing data privacy concerns.

Future research might focus on optimizing federated algorithms further to deal with non-ideal (non-IID) data distributions across clients, which can be critical for achieving more robust and equitable model performance in real-world scenarios. Additionally, exploring the hybrid integration of other cryptographic techniques, such as secure multiparty computation, could offer additional layers of security and efficiency.

In summary, FedShield-LLM presents an advantageous mix of security measures and computational reductions, positioning it as a promising solution for federating LLM tasks effectively in sensitive settings. As federated learning continues to evolve, frameworks like FedShield-LLM will be crucial in deploying AI solutions that respect both privacy and performance imperatives.