Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

By Nuria Rodríguez Barroso, G. Stipcich, Daniel Jiménez-López, José Antonio Ruiz-Millán, Eugenio Martínez-Cámara, Gerardo González-Seco, M. V. Luzón, M. Veganzones, Francisco Herrera. 2020

124 citations recorded by the source corpus.

Abstract The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated learning. The prospective matching of federated learning and differential privacy to the challenges of data privacy protection has caused the release of several software tools that support their functionalities, but they lack a unified vision of these techniques, and a methodological workflow that supports their usage. Hence, we present the Sherpa.ai Federated Learning framework that is built upon a holistic view of federated learning and differential privacy. It results from both the study of how to adapt the machine learning paradigm to federated learning, and the definition of methodological guidelines for developing artificial intelligence services based on federated learning and differential privacy. We show how to follow the methodological guidelines with the Sherpa.ai Federated Learning framework by means of a classification and a regression use cases.

Subfields: Artificial Intelligence, Sociology and Political Science, Federated & Distributed Learning, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Johannes B Reitsma
2. Karel G. M. Moons
3. T. Debray
4. M. van Smeden
5. Lotty Hooft
6. Steven W. J. Nijman
7. E. Schuit
8. Ewout W. Steyerberg

In Sociology and Political Science

1. Ewout W. Steyerberg
2. T. Debray
3. Jiankun Hu
4. Karel G. M. Moons
5. Yanming Zhu
6. Xuefei Yin

In Federated & Distributed Learning

1. Jiankun Hu
2. Yanming Zhu
3. Xuefei Yin

In Privacy & Security

1. Jiankun Hu
2. Yanming Zhu
3. Xuefei Yin

Citations in the corpus

• A Comprehensive Survey of Privacy-preserving Federated Learning (2021, methodology): “The global differential privacy scheme has been widely used in many FL methods [3, 31, 40, 63, 69, 70, 78, 94, 128, 149, 172, 193].”
• Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review (2022, background): “…more robust to these vulnerabilities can be implemented, like converting data to less identi fi able formats 23 , adding random noise to the data 23,34,41 , federated learning 23,34,41 , saving personal data across different databases 34,35 , and adversarial ML techniques such as model hard-ening…”