Privacy-Preserving Federated Learning on Partitioned Attributes
This addresses privacy risks in federated learning for distributed data scenarios, though it is incremental as it builds on existing adversarial defense techniques.
The paper tackled the problem of attribute inference attacks in federated learning, where adversaries can infer input attributes from corrupted data, and introduced an adversarial learning-based defense method that significantly mitigates privacy leakage with negligible impact on accuracy.
Real-world data is usually segmented by attributes and distributed across different parties. Federated learning empowers collaborative training without exposing local data or models. As we demonstrate through designed attacks, even with a small proportion of corrupted data, an adversary can accurately infer the input attributes. We introduce an adversarial learning based procedure which tunes a local model to release privacy-preserving intermediate representations. To alleviate the accuracy decline, we propose a defense method based on the forward-backward splitting algorithm, which respectively deals with the accuracy loss and privacy loss in the forward and backward gradient descent steps, achieving the two objectives simultaneously. Extensive experiments on a variety of datasets have shown that our defense significantly mitigates privacy leakage with negligible impact on the federated learning task.