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

arXiv: https://arxiv.org/abs/2501.19091 · HTML: https://mlsanity.com/paper/2501.19091/

## 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 (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Towards Federated Learning at Scale: System Design](https://mlsanity.com/paper/1902.01046/) (2019)
- [How To Backdoor Federated Learning](https://mlsanity.com/paper/1807.00459/) (2018)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Federated Learning on Non-IID Data Silos: An Experimental Study](https://mlsanity.com/paper/2102.02079/) (2021)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Towards Personalized Federated Learning](https://mlsanity.com/paper/2103.00710/) (2021)
- [Flower: A Friendly Federated Learning Research Framework](https://mlsanity.com/paper/2007.14390/) (2020)
- [Federated Machine Learning: Concept and Applications](https://mlsanity.com/paper/1902.04885/) (2019)
- [Robust Aggregation for Federated Learning](https://mlsanity.com/paper/1912.13445/) (2019)
- [Data Poisoning Attacks Against Federated Learning Systems](https://mlsanity.com/paper/2007.08432/) (2020)
- [NVIDIA FLARE: Federated Learning from Simulation to Real-World](https://mlsanity.com/paper/2210.13291/) (2022)
- [Free-rider Attacks on Model Aggregation in Federated Learning](https://mlsanity.com/paper/2006.11901/) (2020)
- [IBM Federated Learning: an Enterprise Framework White Paper V0.1](https://mlsanity.com/paper/2007.10987/) (2020)
- [Substra: a framework for privacy-preserving, traceable and collaborative Machine Learning](https://mlsanity.com/paper/1910.11567/) (2019)
- [FLRA: A Reference Architecture for Federated Learning Systems](https://mlsanity.com/paper/2106.11570/) (2021)
- [Applied Federated Learning: Architectural Design for Robust and Efficient Learning in Privacy Aware Settings](https://mlsanity.com/paper/2206.00807/) (2022)
- [Fog Computing as Privacy Enabler](https://mlsanity.com/paper/1910.04032/) (2019)

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

- [Research in Collaborative Learning Does Not Serve Cross-Silo Federated Learning in Practice](https://mlsanity.com/paper/2510.12595/) (2025)
- [Analyzing the Impact of Participant Failures in Cross-Silo Federated Learning](https://mlsanity.com/paper/2511.14456/) (2025)
- [Revealing the influence of participant failures on model quality in cross-silo Federated Learning](https://mlsanity.com/paper/2603.25289/) (2026)

## 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.