Byzantine attacks
PulseAugur coverage of Byzantine attacks — every cluster mentioning Byzantine attacks across labs, papers, and developer communities, ranked by signal.
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Federated Learning faces new adversarial attack and defense research · 2 sources tracked
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 …
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Federated Learning Faces New Adversarial Attacks and Defenses
This paper explores the vulnerabilities of federated learning (FL) systems to various adversarial attacks, including poisoning, Byzantine, and adversarial example attacks. Researchers analyzed the transferability of adv…
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New framework tackles hidden Byzantine attacks in multi-agent systems
Researchers have developed a new theoretical framework and algorithm for online security learning in cooperative multi-agent systems facing hidden Byzantine attacks. The study identifies that an attacker's information a…
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New FL-OA framework enhances federated learning against Byzantine attacks
Researchers have introduced FL-OA, a novel federated learning framework designed to enhance robustness against Byzantine attacks. This framework utilizes outsourced auditing with a third-party organization that possesse…
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New WFAgg algorithm enhances security in Decentralized Federated Learning
Researchers have developed a new Byzantine-robust aggregation algorithm called WFAgg for Decentralized Federated Learning (DFL). This algorithm is designed to enhance security in DFL environments by identifying and miti…
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New research tackles Byzantine attacks in Federated Learning
Two new research papers address the challenge of Byzantine attacks in Federated Learning (FL). The first paper introduces Projected Dimensionality Reduction (PDR), a framework designed to accelerate robust aggregation b…
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Researchers propose AdaBFL for robust federated learning against attacks
Researchers have introduced AdaBFL, a novel multi-layer defensive aggregation method designed to enhance the robustness of federated learning against Byzantine attacks. This approach addresses limitations of existing me…