Fedward: Flexible Federated Backdoor Defense Framework with Non-IID Data
Zekai Chen, Fuyi Wang, Zhiwei Zheng, Ximeng Liu, Yujie Lin

TL;DR
Fedward is a flexible federated learning defense framework that effectively eliminates backdoors in non-IID data scenarios by decomposing attacks and employing adaptive defense mechanisms, significantly improving robustness against backdoor attacks.
Contribution
The paper introduces Fedward, a novel framework that decomposes federated backdoor attacks and employs adaptive techniques to eliminate backdoors in non-IID data settings.
Findings
Achieves 96.98% success rate reduction on MNIST
Improves clustering defense effectiveness by 33-75%
Maintains performance in Non-IID scenarios
Abstract
Federated learning (FL) enables multiple clients to collaboratively train deep learning models while considering sensitive local datasets' privacy. However, adversaries can manipulate datasets and upload models by injecting triggers for federated backdoor attacks (FBA). Existing defense strategies against FBA consider specific and limited attacker models, and a sufficient amount of noise to be injected only mitigates rather than eliminates FBA. To address these deficiencies, we introduce a Flexible Federated Backdoor Defense Framework (Fedward) to ensure the elimination of adversarial backdoors. We decompose FBA into various attacks, and design amplified magnitude sparsification (AmGrad) and adaptive OPTICS clustering (AutoOPTICS) to address each attack. Meanwhile, Fedward uses the adaptive clipping method by regarding the number of samples in the benign group as constraints on the…
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Taxonomy
TopicsAdversarial Robustness in Machine Learning · Anomaly Detection Techniques and Applications · Network Security and Intrusion Detection
