# Equitable Federated Learning with NCA

arXiv: https://arxiv.org/abs/2506.21735 · HTML: https://mlsanity.com/paper/2506.21735/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 29 references that are in the map.
- First posted on arXiv 26 Jun 2025.

## Builds on (most cited first, 20 of 20)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [Inverting Gradients -- How easy is it to break privacy in federated learning?](https://mlsanity.com/paper/2003.14053/) (2020)
- [Federated Learning for Smart Healthcare: A Survey](https://mlsanity.com/paper/2111.08834/) (2021)
- [Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach](https://mlsanity.com/paper/2001.04756/) (2020)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devices](https://mlsanity.com/paper/2109.15258/) (2021)
- [FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System](https://mlsanity.com/paper/2303.10837/) (2023)
- [No One Left Behind: Inclusive Federated Learning over Heterogeneous Devices](https://mlsanity.com/paper/2202.08036/) (2022)
- [Source Inference Attacks in Federated Learning](https://mlsanity.com/paper/2109.05659/) (2021)
- [FedCV: A Federated Learning Framework for Diverse Computer Vision Tasks](https://mlsanity.com/paper/2111.11066/) (2021)
- [Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automata](https://mlsanity.com/paper/2302.03473/) (2023)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [FedFMS: Exploring Federated Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2403.05408/) (2024)
- [Preserving Privacy and Security in Federated Learning](https://mlsanity.com/paper/2202.03402/) (2022)
- [FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communications](https://mlsanity.com/paper/2312.13461/) (2023)
- [Unsupervised Training of Neural Cellular Automata on Edge Devices](https://mlsanity.com/paper/2407.18114/) (2024)
- [Intrinisic Gradient Compression for Federated Learning](https://mlsanity.com/paper/2112.02656/) (2021)
- [Communication-Efficient Federated Learning over Capacity-Limited Wireless Networks](https://mlsanity.com/paper/2307.10815/) (2023)
- [Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey](https://mlsanity.com/paper/2303.14544/) (2023)

## Built on by (most cited first, 3 of 3)

- [Sterilizable Scene Graph Generation for Operating Rooms](https://mlsanity.com/paper/2608.16469/) (2026)
- [Coarse to Fine: Iterative Adversarial Neural Cellular Automata for Medical Image Synthesis](https://mlsanity.com/paper/2608.28909/) (2026)
- [Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning](https://mlsanity.com/paper/2608.12108/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is enabling collaborative model training across institutions without sharing sensitive patient data. This approach is particularly valuable in low- and middle-income countries (LMICs), where access to trained medical professionals is limited. However, FL adoption in LMICs faces significant barriers, including limited high-performance computing resources and unreliable internet connectivity. To address these challenges, we introduce FedNCA, a novel FL system tailored for medical image segmentation tasks. FedNCA leverages the lightweight Med-NCA architecture, enabling training on low-cost edge devices, such as widely available smartphones, while minimizing communication costs. Additionally, our encryption-ready FedNCA proves to be suitable for compromised network communication. By overcoming infrastructural and security challenges, FedNCA paves the way for inclusive, efficient, lightweight, and encryption-ready medical imaging solutions, fostering equitable healthcare advancements in resource-constrained regions.