Detection and Prevention Against Poisoning Attacks in Federated Learning
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Abstract (arXiv)
This paper proposes and investigates a new approach for detecting and preventing several different types of poisoning attacks from affecting a centralized Federated Learning model via average accuracy deviation detection (AADD). By comparing each client's accuracy to all clients' average accuracy, AADD detect clients with an accuracy deviation. The implementation is further able to blacklist clients that are considered poisoned, securing the global model from being affected by the poisoned nodes. The proposed implementation shows promising results in detecting poisoned clients and preventing the global model's accuracy from deteriorating.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 30 references that are in the map. First posted on arXiv 24 Oct 2022.
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9 of 30 references.
References · 9
- 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
- 2020The Future of Digital Health with Federated Learningcited by 625
- 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
- 2021Deep Federated Learning for Autonomous Drivingcited by 54
- 2020Federated Learning in Vehicular Networkscited by 53
- 2022FedSpace: An Efficient Federated Learning Framework at Satellites and Ground Stationscited by 28
- 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
- 2021Evaluating Federated Learning for Intrusion Detection in Internet of Things: Review and Challengescited by 16
- 2021Cybersecurity Threats in Connected and Automated Vehicles based Federated Learning Systemscited by 3
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