Federated Model Distillation with Noise-Free Differential Privacy

Federated Model Distillation with Noise-Free Differential Privacy

Lichao Sun, Lingjuan Lyu

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence
Main Track. Pages 1563-1570. https://doi.org/10.24963/ijcai.2021/216

Conventional federated learning directly averages model weights, which is only possible for collaboration between models with homogeneous architectures. Sharing prediction instead of weight removes this obstacle and eliminates the risk of white-box inference attacks in conventional federated learning. However, the predictions from local models are sensitive and would leak training data privacy to the public. To address this issue, one naive approach is adding the differentially private random noise to the predictions, which however brings a substantial trade-off between privacy budget and model performance. In this paper, we propose a novel framework called FEDMD-NFDP, which applies a Noise-FreeDifferential Privacy (NFDP) mechanism into a federated model distillation framework. Our extensive experimental results on various datasets validate that FEDMD-NFDP can deliver not only comparable utility and communication efficiency but also provide a noise-free differential privacy guarantee. We also demonstrate the feasibility of our FEDMD-NFDP by considering both IID and Non-IID settings, heterogeneous model architectures, and unlabelled public datasets from a different distribution.
Keywords:
Data Mining: Federated Learning
Data Mining: Privacy Preserving Data Mining
Agent-based and Multi-agent Systems: Multi-agent Learning
AI Ethics, Trust, Fairness: Trustable Learning