RAB2-DEF: Dynamic and explainable defense against adversarial attacks in Federated Learning to fair poor clients
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Abstract (arXiv)
At the same time that artificial intelligence is becoming popular, concern and the need for regulation is growing, including among other requirements the data privacy. In this context, Federated Learning is proposed as a solution to data privacy concerns derived from different source data scenarios due to its distributed learning. The defense mechanisms proposed in literature are just focused on defending against adversarial attacks and the performance, leaving aside other important qualities such as explainability, fairness to poor quality clients, dynamism in terms of attacks configuration and generality in terms of being resilient against different kinds of attacks. In this work, we propose RAB2-DEF, a resilient against byzantine and backdoor attacks which is dynamic, explainable and fair to poor clients using local linear explanations. We test the performance of RAB2-DEF in image datasets and both byzantine and backdoor attacks considering the state-of-the-art defenses and achieve that RAB2-DEF is a proper defense at the same time that it boosts the other qualities towards trustworthy artificial intelligence.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 46 references that are in the map. First posted on arXiv 10 Oct 2024.
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21 of 46 references.
References · 21
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9 more
- 2021FairFed: Enabling Group Fairness in Federated Learningcited by 118
- 2020Collaborative Fairness in Federated Learningcited by 95
- 2022Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challengescited by 84
- 2020Defending against Backdoors in Federated Learning with Robust Learning Ratecited by 82
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- 2024Position: Explain to Question not to Justifycited by 20
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- 2022REVEL Framework to measure Local Linear Explanations for black-box models: Deep Learning Image Classification case of studycited by 5
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