PCDM uses a poisoning-oriented conditional diffusion model with an adjustable vector and jumping strategy to create stealthier and more effective poisoned data than GAN-based attacks against federated learning.
Manipulating the byzantine: Opti- mizing model poisoning attacks and defenses for federated learning,
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ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA for filtering malicious updates via adaptive thresholds and oracle-based clustering to achieve Byzantine-resilient decentralized learning for EV battery intelligence.
DP2Guard is a proposed lightweight PPFL framework that combines gradient masking for privacy, hybrid anomaly detection via SVD and clustering for Byzantine robustness, trust-based adaptive aggregation, and blockchain logging for Industrial IoT.
citing papers explorer
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PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems
PCDM uses a poisoning-oriented conditional diffusion model with an adjustable vector and jumping strategy to create stealthier and more effective poisoned data than GAN-based attacks against federated learning.
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Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
ABC-DFL replaces central FL servers with a permissioned blockchain and introduces FLECA for filtering malicious updates via adaptive thresholds and oracle-based clustering to achieve Byzantine-resilient decentralized learning for EV battery intelligence.
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DP2Guard: A Lightweight and Byzantine-Robust Privacy-Preserving Federated Learning Scheme for Industrial IoT
DP2Guard is a proposed lightweight PPFL framework that combines gradient masking for privacy, hybrid anomaly detection via SVD and clustering for Byzantine robustness, trust-based adaptive aggregation, and blockchain logging for Industrial IoT.