Federated Parameter-Efficient Fine-Tuning (FedPEFT) is a technique that combines parameter-efficient fine-tuning (PEFT) with federated learning (FL) to improve the efficiency and privacy of training large language models (PLMs) on specific tasks. However, this approach introduces a new security risk called “PEFT-as-an-Attack” (PaaA), where malicious actors can exploit PEFT to bypass the safety alignment of PLMs […]
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