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

by   Nuria Rodríguez-Barroso, et al.

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 those requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in their data 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 of the needed unified vision for those techniques, and a methodological workflow that support their use. Hence, we present the Sherpa.ai Federated Learning framework that is built upon an holistic view of federated learning and differential privacy. It results from 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.


page 20

page 22

page 32

page 34

page 40


Federated Learning with Bayesian Differential Privacy

We consider the problem of reinforcing federated learning with formal pr...

A Generative Federated Learning Framework for Differential Privacy

In machine learning, differential privacy and federated learning concept...

WebFed: Cross-platform Federated Learning Framework Based on Web Browser with Local Differential Privacy

For data isolated islands and privacy issues, federated learning has bee...

Federated Crowdsensing: Framework and Challenges

Crowdsensing is a promising sensing paradigm for smart city applications...

Federated Learning for Privacy-Preserving Open Innovation Future on Digital Health

Privacy protection is an ethical issue with broad concern in Artificial ...

Survey on Federated Learning Threats: concepts, taxonomy on attacks and defences, experimental study and challenges

Federated learning is a machine learning paradigm that emerges as a solu...

OpenFed: An Open-Source Security and Privacy Guaranteed Federated Learning Framework

The broad application of artificial intelligence techniques ranging from...

Please sign up or login with your details

Forgot password? Click here to reset