# Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applications

arXiv: https://arxiv.org/abs/2409.00974 · HTML: https://mlsanity.com/paper/2409.00974/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 24 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 7 of 7)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (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)
- [FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings](https://mlsanity.com/paper/2210.04620/) (2022)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications](https://mlsanity.com/paper/2304.12012/) (2023)
- [Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentation](https://mlsanity.com/paper/2309.17097/) (2023)

## Built on by (most cited first, 4 of 4)

- [Don't Reach for the Stars: Rethinking Topology for Resilient Federated Learning](https://mlsanity.com/paper/2508.05224/) (2025)
- [Adaptive Federated Few-Shot Rare-Disease Diagnosis with Energy-Aware Secure Aggregation](https://mlsanity.com/paper/2510.00976/) (2025)
- [Secure Multi-Modal Data Fusion in Federated Digital Health Systems via MCP](https://mlsanity.com/paper/2510.01780/) (2025)
- [Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning](https://mlsanity.com/paper/2608.12108/) (2026)

## Abstract (arXiv)

Deploying federated learning (FL) in real-world scenarios, particularly in healthcare, poses challenges in communication and security. In particular, with respect to the federated aggregation procedure, researchers have been focusing on the study of secure aggregation (SA) schemes to provide privacy guarantees over the model's parameters transmitted by the clients. Nevertheless, the practical availability of SA in currently available FL frameworks is currently limited, due to computational and communication bottlenecks. To fill this gap, this study explores the implementation of SA within the open-source Fed-BioMed framework. We implement and compare two SA protocols, Joye-Libert (JL) and Low Overhead Masking (LOM), by providing extensive benchmarks in a panel of healthcare data analysis problems. Our theoretical and experimental evaluations on four datasets demonstrate that SA protocols effectively protect privacy while maintaining task accuracy. Computational overhead during training is less than 1% on a CPU and less than 50% on a GPU for large models, with protection phases taking less than 10 seconds. Incorporating SA into Fed-BioMed impacts task accuracy by no more than 2% compared to non-SA scenarios. Overall this study demonstrates the feasibility of SA in real-world healthcare applications and contributes in reducing the gap towards the adoption of privacy-preserving technologies in sensitive applications.