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New framework exposes privacy risks in federated LoRA-adapted Vision Transformers

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new framework to address privacy risks in federated learning when using parameter-efficient fine-tuning methods like LoRA with Vision Transformers. The proposed spatially anchored generative inversion framework enhances the reconstruction quality of private data by incorporating trainable positional embeddings into the ViT blocks. This method significantly improves image reconstruction metrics such as PSNR, SSIM, and LPIPS on datasets like CIFAR-10, CelebA, and CARS, demonstrating that LoRA-based fine-tuning alone does not inherently prevent data leakage in federated learning. AI

IMPACT Highlights potential privacy vulnerabilities in federated learning setups using efficient fine-tuning techniques for vision models.

RANK_REASON Research paper detailing a new method for gradient inversion attacks on federated learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework exposes privacy risks in federated LoRA-adapted Vision Transformers

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Research paper detailing a new method for gradient inversion attacks on federated learning models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Ritik K. Badiya, Siladittya Manna, Ashmit Sinha, M Yashwanth, Anirban Chakraborty ·

    Breaking the Privacy Promise: Gradient Inversion Attacks on Federated Low-Rank Adapted Vision Transformers

    arXiv:2611.09652v1 Announce Type: new Abstract: Parameter-efficient fine-tuning (PEFT) has been increasingly adopted to fine-tune pretrained models with low computational cost and is therefore also preferred in the federated learning paradigm to reduce computational and communica…

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