FedDOT: Defending Federated Learning Against Overwhelming Targeted Attacks

Summary

FedDOT is a federated learning framework that defends against targeted attacks, including attacks by adversaries who make up more than half of the participants. It combines two detection algorithms, MST-AD (maximum spanning tree-based attacker detection) and density-AD (densest graph-based attacker detection), which use correlation between weight updates and graph theory. Experiments on image classification datasets in non-IID settings report an attack success rate below 10% for single-label flipping, multilabel flipping and backdoor attacks, with an accuracy drop under 2%.

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