Data-driven decision-making powered by Machine Learning (ML) algorithms is changing the way society and the economy work, and is having a profound positive impact on our daily lives. In fact, ML applications are becoming even more ubiquitous and integrated, often invisibly, into our daily activities, having a direct impact on things like how we find our way around a city, how we decide what to buy or where to eat, while at the same time we can keep ourselves safe from financial fraud, or have tools that remind us to take medication or suggest new personalized habits for a healthier lifestyle.

However, for ML-based solutions to be effective at such tasks, data often has to be processed close to the end user. Furthermore, such data may be private and of a confidential nature. Distributed Learning and, in particular, Federated Learning (FL: Federated Learning) emerges as a leading paradigm within the ML branch satisfying these two properties. FL has grown in parallel with the expansion of cloud to the edge (CloudEdge) but, interestingly, both paradigms have mostly developed independently, despite their natural parallelism and potential synergistic gains.

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.

This project (REGAGE22e00052829516) has been funded by the Ministry of Digital Transformation and Civil Service and the European Union-NextGenerationEU/PRTR.