# Secure Aggregation for Federated Learning in Flower

arXiv: https://arxiv.org/abs/2205.06117 · HTML: https://mlsanity.com/paper/2205.06117/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 31 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 12 of 12)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Practical Secure Aggregation for Federated Learning on User-Held Data](https://mlsanity.com/paper/1611.04482/) (2016)
- [CrypTen: Secure Multi-Party Computation Meets Machine Learning](https://mlsanity.com/paper/2109.00984/) (2021)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning](https://mlsanity.com/paper/2002.04156/) (2020)
- [FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning](https://mlsanity.com/paper/2009.11248/) (2020)
- [Spectre Attacks: Exploiting Speculative Execution](https://mlsanity.com/paper/1801.01203/) (2018)
- [Communication-Computation Efficient Secure Aggregation for Federated Learning](https://mlsanity.com/paper/2012.05433/) (2020)

## Built on by (most cited first, 15 of 15)

- [Machine Learning for Microcontroller-Class Hardware: A Review](https://mlsanity.com/paper/2205.14550/) (2022)
- [ScionFL: Efficient and Robust Secure Quantized Aggregation](https://mlsanity.com/paper/2210.07376/) (2022)
- [Protea: Client Profiling within Federated Systems using Flower](https://mlsanity.com/paper/2207.01053/) (2022)
- [CATFL: Certificateless Authentication-based Trustworthy Federated Learning for 6G Semantic Communications](https://mlsanity.com/paper/2302.00271/) (2023)
- [Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy](https://mlsanity.com/paper/2501.12537/) (2025)
- [Technical and Legal Aspects of Federated Learning in Bioinformatics: Applications, Challenges and Opportunities](https://mlsanity.com/paper/2503.09649/) (2025)
- [On the Security and Privacy of Federated Learning: A Survey with Attacks, Defenses, Frameworks, Applications, and Future Directions](https://mlsanity.com/paper/2508.13730/) (2025)
- [Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction](https://mlsanity.com/paper/2504.18007/) (2025)
- [Distribution-Controlled Client Selection to Improve Federated Learning Strategies](https://mlsanity.com/paper/2509.20877/) (2025)
- [Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment](https://mlsanity.com/paper/2308.11220/) (2023)
- [MetisFL: An Embarrassingly Parallelized Controller for Scalable & Efficient Federated Learning Workflows](https://mlsanity.com/paper/2311.00334/) (2023)
- [Federated Foundation Language Model Post-Training Should Focus on Open-Source Models](https://mlsanity.com/paper/2505.23593/) (2025)
- [PrivacyBench: Privacy Isn't Free in Hybrid Privacy-Preserving Vision Systems](https://mlsanity.com/paper/2602.18900/) (2026)
- [FPLIER: Federated Pathway-Level Information Extractor](https://mlsanity.com/paper/2605.29587/) (2026)
- [Defining Decentralization: An Ontological Perspective](https://mlsanity.com/paper/2608.09748/) (2026)

## Abstract (arXiv)

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.