MLEDGE: Cloud and Edge Machine Learning

In this project, Cloud and Edge Machine Learning (MLEDGE), we will work to reverse this trend by deploying FL as a standalone but optimized cross-industry layer on top of CloudEdge, using real-world data and applications to demonstrate that this synergy can produce great benefits for all. MLEDGE aims to enable a thriving ecosystem of secure and efficient ML edge services capable of facilitating the use of sensitive personal and B2B data to train ML models for consumers while protecting the privacy of the data and its owners. Recent studies in the field of the “European Data Strategy” estimated that the data economy will reach an impact of 827 billion euros for the EU27 as early as 2025. However, even today privacy concerns and property hinder their full development. MLEDGE will be instrumental in increasing these projections in the period 2025-2030.

My role in the project is to design and develop FL to the cloud edge, focusing on the privacy-preserving and Byzantine-robust aspects of the aggregation. The mechanism will be integrated into the MLEDGE platform and will be evaluated using real-world data and applications.