SketchGuard decouples Byzantine filtering from aggregation in decentralized federated learning by exchanging k-dimensional Count Sketches for screening and full models only from accepted neighbors, achieving up to 50-70% communication savings while proving convergence and matching SOTA robustness.
Byzantine-robust aggregation for s ecuring decentralized federated learning
2 Pith papers cite this work. Polarity classification is still indexing.
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Builds a hardware FL testbed for cybersecurity intrusion detection and evaluates its vulnerability to poisoning attacks.
citing papers explorer
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SketchGuard: Scaling Byzantine-Robust Decentralized Federated Learning via Sketch-Based Screening
SketchGuard decouples Byzantine filtering from aggregation in decentralized federated learning by exchanging k-dimensional Count Sketches for screening and full models only from accepted neighbors, achieving up to 50-70% communication savings while proving convergence and matching SOTA robustness.
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Federated Learning in Adversarial Environments: Testbed Design and Poisoning Resilience in Cybersecurity
Builds a hardware FL testbed for cybersecurity intrusion detection and evaluates its vulnerability to poisoning attacks.