mlsanityData to 7 Oct 2026

Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications

arXiv 2304.12012 ↗First posted 24 Apr 2023cs.LG cs.DCcited by 10

Not on the map. Why?

Abstract (arXiv)

The real-world implementation of federated learning is complex and requires research and development actions at the crossroad between different domains ranging from data science, to software programming, networking, and security. While today several FL libraries are proposed to data scientists and users, most of these frameworks are not designed to find seamless application in medical use-cases, due to the specific challenges and requirements of working with medical data and hospital infrastructures. Moreover, governance, design principles, and security assumptions of these frameworks are generally not clearly illustrated, thus preventing the adoption in sensitive applications. Motivated by the current technological landscape of FL in healthcare, in this document we present Fed-BioMed: a research and development initiative aiming at translating federated learning (FL) into real-world medical research applications. We describe our design space, targeted users, domain constraints, and how these factors affect our current and future software architecture.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 15 of its 64 references that are in the map. First posted on arXiv 24 Apr 2023.

Builds on

15 of 64 references.

References · 15

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Towards Federated Learning at Scale: System Designcited by 826
  3. 2020The Future of Digital Health with Federated Learningcited by 625
  4. 2021The Medical Segmentation Decathloncited by 620
  5. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 449
  6. 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 371
  7. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  8. 2018A generic framework for privacy preserving deep learningcited by 103
  9. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  10. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 78
  11. 2018A Review of Challenges and Opportunities in Machine Learning for Healthcited by 67
  12. 2020Federated Learning for Breast Density Classification: A Real-World Implementationcited by 62
3 more
  1. 2021OpenFL: An open-source framework for Federated Learningcited by 58
  2. 2020IBM Federated Learning: an Enterprise Framework White Paper V0.1cited by 57
  3. 2019Substra: a framework for privacy-preserving, traceable and collaborative Machine Learningcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 3.

10 papers.

  1. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  2. 2024The Federation Strikes Back: A Survey of Federated Learning Privacy Attacks, Defenses, Applications, and Policy Landscapecited by 13
  3. 2023When Federated Learning meets Watermarking: A Comprehensive Overview of Techniques for Intellectual Property Protectioncited by 6
  4. 2024Enhancing Privacy in Federated Learning: Secure Aggregation for Real-World Healthcare Applicationscited by 4
  5. 2024Federated and Transfer Learning for Cancer Detection Based on Image Analysiscited by 3
  6. 2026LatentDx: Latent Multi-Agent Communication for Cross-Hospital Rare-Disease Diagnosiscited by 1
  7. 2024A cautionary tale on the cost-effectiveness of collaborative AI in real-world medical applicationscited by 0
  8. 2025FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learningcited by 0
  9. 2026Pushing Forward Multi-Secret-Key Homomorphic Encryption for Private Average Aggregationcited by 0
  10. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0