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New FedCVESA attack steals private data from federated learning models

By PulseAugur Editorial · [3 sources] ·

Researchers have developed FedCVESA, a novel method to conduct "Taking Away Training Data" (TATD) attacks within federated learning environments. This white-box attack targets specific clients to encode private training data into model parameters, referred to as carrier parameters. To counteract the overwriting that occurs during standard server aggregation in federated learning, FedCVESA employs segmented aggregation, preserving these critical carrier parameters while allowing normal averaging for the rest. Experiments on datasets like MNIST and CIFAR-10 demonstrated that FedCVESA can successfully extract meaningful private training images, even under non-IID data distributions, while maintaining acceptable utility for the main task. AI

IMPACT Highlights significant privacy risks in federated learning, potentially impacting the adoption and security of distributed AI systems.

RANK_REASON The cluster contains two identical arXiv papers detailing a new method for data privacy attacks in federated learning.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

New FedCVESA attack steals private data from federated learning models

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The cluster contains two identical arXiv papers detailing a new method for data privacy attacks in federated learning.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Chongkai Li, Bang Zhang, Wenjian Luo ·

    FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

    arXiv:2607.07314v1 Announce Type: cross Abstract: Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) atta…

  2. arXiv cs.AI TIER_1 English(EN) · Wenjian Luo ·

    FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

    Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse…

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    FedCVESA: Taking Away Training Data in Federated Learning via Correlation Value Encoding and Segmented Aggregation

    Federated learning (FL) avoids explicit data exposure by keeping raw data on local clients, yet privacy risks remain in the training process and the learned model itself. Recently, centralized Taking Away Training Data (TATD) attacks have shown that malicious training could abuse…

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