mlsanityData to 14 Oct 2026

Equitable Federated Learning with NCA

arXiv 2506.21735 ↗First posted 26 Jun 2025cs.CVcited by 3

Not on the map. Why?

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.

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

20 of 29 references.

References · 20

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,558
  3. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 712
  4. 2021Federated Learning for Smart Healthcare: A Surveycited by 184
  5. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 81
  6. 2023Federated Learning for Medical Image Analysis: A Surveycited by 78
  7. 2021Federated Dropout -- A Simple Approach for Enabling Federated Learning on Resource Constrained Devicescited by 56
  8. 2023FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning Systemcited by 38
  9. 2022No One Left Behind: Inclusive Federated Learning over Heterogeneous Devicescited by 37
  10. 2021Source Inference Attacks in Federated Learningcited by 34
  11. 2021FedCV: A Federated Learning Framework for Diverse Computer Vision Taskscited by 32
  12. 2023Med-NCA: Robust and Lightweight Segmentation with Neural Cellular Automatacited by 24
8 more
  1. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  2. 2024FedFMS: Exploring Federated Foundation Models for Medical Image Segmentationcited by 21
  3. 2022Preserving Privacy and Security in Federated Learningcited by 13
  4. 2023FedSZ: Leveraging Error-Bounded Lossy Compression for Federated Learning Communicationscited by 4
  5. 2024Unsupervised Training of Neural Cellular Automata on Edge Devicescited by 4
  6. 2021Intrinisic Gradient Compression for Federated Learningcited by 2
  7. 2023Communication-Efficient Federated Learning over Capacity-Limited Wireless Networkscited by 2
  8. 2023Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Surveycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Sterilizable Scene Graph Generation for Operating Roomscited by 1
  2. 2026Coarse to Fine: Iterative Adversarial Neural Cellular Automata for Medical Image Synthesiscited by 1
  3. 2026Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learningcited by 0