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FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients.

, , , and . KDD, page 2545-2555. ACM, (2022)

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FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping., , , and . CoRR, (2020)Hydrogen-Based Networked Microgrids Planning Through Two-Stage Stochastic Programming With Mixed-Integer Conic Recourse., , , , and . IEEE Trans Autom. Sci. Eng., 19 (4): 3672-3685 (2022)Local Model Poisoning Attacks to Byzantine-Robust Federated Learning., , , and . USENIX Security Symposium, page 1605-1622. USENIX Association, (2020)FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients., , , and . KDD, page 2545-2555. ACM, (2022)Local Model Poisoning Attacks to Byzantine-Robust Federated Learning., , , and . CoRR, (2019)Understanding the Security of Deepfake Detection., and . ICDF2C, volume 441 of Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, page 360-378. Springer, (2021)FedRecover: Recovering from Poisoning Attacks in Federated Learning using Historical Information., , , and . SP, page 1366-1383. IEEE, (2023)Certified Robustness for Top-k Predictions against Adversarial Perturbations via Randomized Smoothing., , , and . ICLR, OpenReview.net, (2020)Data Poisoning Attacks to Local Differential Privacy Protocols., , and . CoRR, (2019)Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations., , , , and . CoRR, (2020)