共 50 条
- [1] MATFL: Defending Against Synergetic Attacks in Federated Learning 2023 IEEE INTERNATIONAL CONFERENCES ON INTERNET OF THINGS, ITHINGS IEEE GREEN COMPUTING AND COMMUNICATIONS, GREENCOM IEEE CYBER, PHYSICAL AND SOCIAL COMPUTING, CPSCOM IEEE SMART DATA, SMARTDATA AND IEEE CONGRESS ON CYBERMATICS,CYBERMATICS, 2024, : 313 - 319
- [2] CONTRA: Defending Against Poisoning Attacks in Federated Learning COMPUTER SECURITY - ESORICS 2021, PT I, 2021, 12972 : 455 - 475
- [3] Defending Against Targeted Poisoning Attacks in Federated Learning 2022 IEEE 4TH INTERNATIONAL CONFERENCE ON TRUST, PRIVACY AND SECURITY IN INTELLIGENT SYSTEMS, AND APPLICATIONS, TPS-ISA, 2022, : 198 - 207
- [4] Defending Against Poisoning Attacks in Federated Learning with Blockchain IEEE Transactions on Artificial Intelligence, 2024, 5 (07): : 1 - 13
- [5] Defending Against Byzantine Attacks in Quantum Federated Learning 2021 17TH INTERNATIONAL CONFERENCE ON MOBILITY, SENSING AND NETWORKING (MSN 2021), 2021, : 145 - 152
- [6] Defending against Adversarial Attacks in Federated Learning on Metric Learning Model 2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023, 2024, : 197 - 206
- [7] DEFENDING AGAINST BACKDOOR ATTACKS IN FEDERATED LEARNING WITH DIFFERENTIAL PRIVACY 2022 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2022, : 2999 - 3003
- [10] Defending Against Data and Model Backdoor Attacks in Federated Learning IEEE INTERNET OF THINGS JOURNAL, 2024, 11 (24): : 39276 - 39294