Secure Aggregation for Federated Learning in Flower

TL;DR

This paper introduces Salvia, a secure aggregation protocol implementation for federated learning in Flower, which is robust against client dropouts and easy to integrate with various machine learning frameworks.

Contribution

Salvia provides a practical, dropout-robust secure aggregation solution for federated learning frameworks, enhancing privacy and usability.

Findings

Salvia's performance aligns with theoretical complexity predictions.

It is compatible with multiple machine learning frameworks.

Salvia effectively handles client dropouts in federated learning.

Abstract

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.