Privacy-Preserving Federated Learning via Dataset Distillation
This addresses privacy concerns for users in federated learning systems, though it is incremental as it builds on existing dataset distillation techniques.
The paper tackles the problem of data privacy in Federated Learning by proposing FLiP, a method that minimizes shared knowledge through dataset distillation, achieving a balance between model accuracy and privacy protection.
Federated Learning (FL) allows users to share knowledge instead of raw data to train a model with high accuracy. Unfortunately, during the training, users lose control over the knowledge shared, which causes serious data privacy issues. We hold that users are only willing and need to share the essential knowledge to the training task to obtain the FL model with high accuracy. However, existing efforts cannot help users minimize the shared knowledge according to the user intention in the FL training procedure. This work proposes FLiP, which aims to bring the principle of least privilege (PoLP) to FL training. The key design of FLiP is applying elaborate information reduction on the training data through a local-global dataset distillation design. We measure the privacy performance through attribute inference and membership inference attacks. Extensive experiments show that FLiP strikes a good balance between model accuracy and privacy protection.