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- [1] FLARE: Defending Federated Learning against Model Poisoning Attacks via Latent Space Representations ASIA CCS'22: PROCEEDINGS OF THE 2022 ACM ASIA CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, 2022, : 946 - 958
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- [3] Defending Against Poisoning Attacks in Federated Learning with Blockchain IEEE Transactions on Artificial Intelligence, 2024, 5 (07): : 1 - 13
- [4] CONTRA: Defending Against Poisoning Attacks in Federated Learning COMPUTER SECURITY - ESORICS 2021, PT I, 2021, 12972 : 455 - 475
- [5] FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients PROCEEDINGS OF THE 28TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, KDD 2022, 2022, : 2545 - 2555
- [6] DeFL: Defending against Model Poisoning Attacks in Federated Learning via Critical Learning Periods Awareness THIRTY-SEVENTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 37 NO 9, 2023, : 10711 - 10719
- [8] FedEqual: Defending Model Poisoning Attacks in Heterogeneous Federated Learning 2021 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2021,
- [10] Defending Against Data Poisoning Attacks: From Distributed Learning to Federated Learning COMPUTER JOURNAL, 2023, 66 (03): : 711 - 726