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Federated Learning faces new adversarial attack and defense research · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Two recent arXiv papers explore the challenges of adversarial attacks and defenses within federated learning (FL) frameworks. The first paper investigates the feasibility of adversarial training for Vision Transformers in FL, proposing a new aggregation method called FedWAvg to improve robust accuracy on non-IID data. The second paper analyzes the transferability of adversarial examples across different client models in FL and introduces a defense mechanism based on adversarial training to mitigate these attacks, offering theoretical insights into system vulnerabilities. AI

IMPACT New research highlights vulnerabilities in federated learning systems and proposes novel defense mechanisms against adversarial attacks.

RANK_REASON Two academic papers published on arXiv discussing adversarial attacks and defenses in federated learning.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Federated Learning faces new adversarial attack and defense research · 2 sources tracked

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Two academic papers published on arXiv discussing adversarial attacks and defenses in federated learning.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Ahmed Aldahdooh, Wassim Hamidouche, Olivier D\'eforges ·

    Federated Adversarial Training with Transformers

    arXiv:2206.02131v2 Announce Type: replace Abstract: Federated learning (FL) has emerged to enable global model training over distributed clients' data while preserving its privacy. However, the global trained model is vulnerable to the evasion attacks especially, the adversarial …

  2. arXiv cs.LG TIER_1 English(EN) · Zuobin Xiong, Deval Mukherjee, Homook Cho, Wei Li ·

    Rethinking the Transferable Adversarial Attacks and Robust Defense in Federated Learning

    arXiv:2608.25133v1 Announce Type: new Abstract: The development of federated learning (FL) techniques has helped improve the privacy preservation of users' data and extended the applications of machine learning models. However, the involvement of a large number of users in FL als…

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