Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.
arXiv preprint arXiv:2212.01976 (2022)
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PenTiDef integrates distributed differential privacy, autoencoder-based latent semantic representations with CKA and K-Means clustering for malicious update detection, and blockchain-orchestrated secure FedAvg to deliver higher detection accuracy and F1-score than FLARE and FedCC under up to 40%
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Robust Server Defense Against Unreliable Clients in One-Shot Fair Collaborative Machine Learning
Bilevel optimization learns client weights to defend fairness in one-shot collaborative ML by anchoring to a small trusted root dataset at the server.
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PenTiDef: Decentralized Federated Intrusion Detection System with Differential Privacy and Latent-Space Defense via Blockchain Coordination in IIoT
PenTiDef integrates distributed differential privacy, autoencoder-based latent semantic representations with CKA and K-Means clustering for malicious update detection, and blockchain-orchestrated secure FedAvg to deliver higher detection accuracy and F1-score than FLARE and FedCC under up to 40%