# SAFE: Secure Aggregation with Failover and Encryption

arXiv: https://arxiv.org/abs/2108.05475 · HTML: https://mlsanity.com/paper/2108.05475/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Aug 2021.

## Built on by (most cited first, 2 of 2)

- [Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges](https://mlsanity.com/paper/2201.08135/) (2022)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)

## Abstract (arXiv)

We propose and experimentally evaluate a novel secure aggregation algorithm targeted at cross-organizational federated learning applications with a fixed set of participating learners. Our solution organizes learners in a chain and encrypts all traffic to reduce the controller of the aggregation to a mere message broker. We show that our algorithm scales better and is less resource demanding than existing solutions, while being easy to implement on constrained platforms. With 36 nodes our method outperforms state-of-the-art secure aggregation by 70x, and 56x with and without failover, respectively.