mlsanityData to 14 Oct 2026

FL-APU: A Software Architecture to Ease Practical Implementation of Cross-Silo Federated Learning

arXiv 2501.19091 ↗First posted 31 Jan 2025cs.DC cs.LGcited by 3

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Abstract (arXiv)

Federated Learning (FL) is an upcoming technology that is increasingly applied in real-world applications. Early applications focused on cross-device scenarios, where many participants with limited resources train machine learning (ML) models together, e.g., in the case of Google's GBoard. Contrarily, cross-silo scenarios have only few participants but with many resources, e.g., in the healthcare domain. Despite such early efforts, FL is still rarely used in practice and best practices are, hence, missing. For new applications, in our case inter-organizational cross-silo applications, overcoming this lack of role models is a significant challenge. In order to ease the use of FL in real-world cross-silo applications, we here propose a scenario-based architecture for the practical use of FL in the context of multiple companies collaborating to improve the quality of their ML models. The architecture emphasizes the collaboration between the participants and the FL server and extends basic interactions with domain-specific features. First, it combines governance with authentication, creating an environment where only trusted participants can join. Second, it offers traceability of governance decisions and tracking of training processes, which are also crucial in a production environment. Beyond presenting the architectural design, we analyze requirements for the real-world use of FL and evaluate the architecture with a scenario-based analysis method.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 31 Jan 2025.

Builds on

18 of 34 references.

References · 18

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2019Towards Federated Learning at Scale: System Designcited by 832
  3. 2018How To Backdoor Federated Learningcited by 765
  4. 2018Federated Learning for Mobile Keyboard Predictioncited by 572
  5. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 568
  6. 2018Exploiting Unintended Feature Leakage in Collaborative Learningcited by 520
  7. 2021Towards Personalized Federated Learningcited by 464
  8. 2020Flower: A Friendly Federated Learning Research Frameworkcited by 455
  9. 2019Federated Machine Learning: Concept and Applicationscited by 412
  10. 2019Robust Aggregation for Federated Learningcited by 317
  11. 2020Data Poisoning Attacks Against Federated Learning Systemscited by 275
  12. 2022NVIDIA FLARE: Federated Learning from Simulation to Real-Worldcited by 80
6 more
  1. 2020Free-rider Attacks on Model Aggregation in Federated Learningcited by 57
  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
  4. 2021FLRA: A Reference Architecture for Federated Learning Systemscited by 9
  5. 2022Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settingscited by 7
  6. 2019Fog Computing as Privacy Enablercited by 2

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practicecited by 3
  2. 2025Analyzing the Impact of Participant Failures in Cross-Silo Federated Learningcited by 2
  3. 2026Revealing the influence of participant failures on model quality in cross-silo Federated Learningcited by 0