Secure Aggregation for Federated Learning in Flower
This work addresses secure aggregation for federated learning users, but it is incremental as it implements existing protocols in a specific framework.
The paper tackles the problem of secure aggregation in federated learning by presenting Salvia, an implementation for the Flower framework that is robust against client dropouts and offers an easy-to-use API, with experimental performance matching theoretical complexities.
Federated Learning (FL) allows parties to learn a shared prediction model by delegating the training computation to clients and aggregating all the separately trained models on the server. To prevent private information being inferred from local models, Secure Aggregation (SA) protocols are used to ensure that the server is unable to inspect individual trained models as it aggregates them. However, current implementations of SA in FL frameworks have limitations, including vulnerability to client dropouts or configuration difficulties. In this paper, we present Salvia, an implementation of SA for Python users in the Flower FL framework. Based on the SecAgg(+) protocols for a semi-honest threat model, Salvia is robust against client dropouts and exposes a flexible and easy-to-use API that is compatible with various machine learning frameworks. We show that Salvia's experimental performance is consistent with SecAgg(+)'s theoretical computation and communication complexities.