In the exploration of security vulnerabilities in Federated Learning (FL), researchers uncover a critical weakness in Federated Parameter-Efficient Fine-Tuning (FedPEFT), a method specifically designed for large language models (LLMs).
FedPEFT offers a compelling combination of privacy and efficiency by leveraging FL’s inherent privacy preservation and Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA.
By significantly reducing the number of trainable parameters compared to conventional fine-tuning, it enables efficient training on resource-constrained devices like smartphones.
It exposes a critical vulnerability where malicious actors can strategically manipulate the federated aggregated stage, where local model updates from devices are combined.
By crafting adversarial updates that exploit properties of PEFT, attackers can exert undue influence on the LLM’s training direction, potentially steering it towards generating outputs that align with their objectives, such as spreading misinformation or injecting bias.
The research sheds light on the inherent tension between privacy and security in FL. While FedPEFT offers privacy benefits by keeping training data on user devices, it introduces new attack vectors.
Beyond identifying vulnerabilities, it emphasizes the importance of prioritizing security alongside efficiency and privacy enhancements during the design of FL techniques, especially those incorporating parameter reduction methods like PEFT.
Attackers can potentially manipulate the training of LLMs to generate outputs conforming to their malicious goals, which paves the way for the development of more secure and robust FL techniques for training LLMs.
By shedding light on these vulnerabilities, Li et al. open new avenues for research into securing FL techniques that leverage PEFT for improved efficiency, as their work compels researchers to prioritize the development of robust defenses against adversarial attacks in FL, ensuring the integrity and security of the training process for LLMs.
A deeper understanding of the inherent security risks associated with PEFT’s parameter reduction techniques is crucial for designing provably secure FL protocols, which could involve leveraging formal methods from cryptography to reason about the security guarantees of FL under adversarial settings.
Investigating the possibility of incorporating Byzantine Fault Tolerance (BFT) mechanisms into FL architectures could enhance resilience against malicious actors, which can ensure that even in the presence of a certain number of faulty or malicious devices, the FL algorithm can still converge to a consistent and correct outcome.
, researchers uncover a critical weakness in Federated Parameter-Efficient Fine-Tuning (FedPEFT).){kind=link}