In recent years, Federated Learning (FL) has emerged as a revolutionary privacy-enhancing technology and, consequently, has quickly expanded to other applications. However, further research has cast a shadow of doubt on the strength of privacy protection provided by FL. Potential vulnerabilities and threats pointed out by researchers included a curious aggregator threat; susceptibility to man-in-the-middle and insider attacks that disrupt the convergence of global and local models or cause convergence to fake minima; and, most importantly, inference attacks that aim to re-identify data subjects from FL’s AI model parameter updates.
The goal of TRUMPET is to research and develop novel privacy enhancement methods for Federated Learning, and to deliver a highly scalable Federated AI service platform for researchers, that will enable AI-powered studies of siloed, multi-site, cross-domain, cross border European datasets with privacy guarantees that exceed the requirements of GDPR.
The generic TRUMPET platform will be piloted, demonstrated and validated in the specific use case of European cancer hospitals, allowing researchers and policymakers to extract AI-driven insights from previously inaccessible cross-border, cross-organization cancer data, while ensuring the patients’ privacy. The strong privacy protection accorded by the platform will be verified through the engagement of external experts for independent privacy leakage and re-identification testing.
A secondary goal is to research, develop and promote with EU data protection authorities a novel metric and tool for the certification of GDPR compliance of FL implementations.
Timelex is the legal partner in this four-year project, supporting the TRUMPET consortium in the analysis of legal and ethical issues related to the development of the TRUMPET federated learning platform. As part of this tasks, Timelex also provides legal support to the project clinical partners during the pilot. Timelex will also support the regulatory acceptance of the project outcomes and provide legal support in the development of an armored federated learning privacy metric.
Funded by the European Union. This project has received funding from the European Commission’s Horizon Europe programme under Grant Agreement No. 101070038. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Commission. Neither the European Union nor the granting authority can be held responsible for them.