Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
This addresses security risks in federated learning for applications like healthcare or finance, though it is an incremental improvement over existing defenses.
The paper tackles the vulnerability of federated learning to client-side training data distribution inference attacks by proposing InferGuard, a Byzantine-robust aggregation rule that identifies and mitigates malicious model updates, resulting in high effectiveness and outperforming ten baseline methods across five benchmark datasets.
Recent studies have revealed that federated learning (FL), once considered secure due to clients not sharing their private data with the server, is vulnerable to attacks such as client-side training data distribution inference, where a malicious client can recreate the victim's data. While various countermeasures exist, they are not practical, often assuming server access to some training data or knowledge of label distribution before the attack. In this work, we bridge the gap by proposing InferGuard, a novel Byzantine-robust aggregation rule aimed at defending against client-side training data distribution inference attacks. In our proposed InferGuard, the server first calculates the coordinate-wise median of all the model updates it receives. A client's model update is considered malicious if it significantly deviates from the computed median update. We conduct a thorough evaluation of our proposed InferGuard on five benchmark datasets and perform a comparison with ten baseline methods. The results of our experiments indicate that our defense mechanism is highly effective in protecting against client-side training data distribution inference attacks, even against strong adaptive attacks. Furthermore, our method substantially outperforms the baseline methods in various practical FL scenarios.