After two and a half years of work, the MLEDGE project (Cloud and Edge Machine Learning), led by Professor Nikolaos Laoutaris at IMDEA Networks, has demonstrated that it is possible to combine federated learning with cloud and edge computing infrastructures to develop artificial intelligence solutions that are more secure, efficient, and closer to end users. The project’s results have been translated into real-world applications in both the traditional and digital economy.

The project has developed and tested concrete applications that clearly illustrate its impact. First, real-time COVID risk maps were produced in collaboration with Orange and Acuratio, enabling authorities to take fast and well-informed decisions in the event of in the event of future health crises.

In addition, together with Inmarepro and Acuratio, MLEDGE enabled the optimization of energy consumption in industry by connecting steam pumps from four industrial sites. This approach improves energy efficiency without compromising data privacy and contributes to environmental protection. Inmarepro is currently developing a commercial offering for its customers based on the results and technology transferred through MLEDGE.

Among the project’s main advances are:

• FedQV, a new algorithm inspired by the concept of quadratic voting, which improves the way artificial intelligence models trained on different devices are combined.
• PriPrune, a system that improves their performance without compromising the privacy of users’ data.
• The integration of these innovations into a commercial federated learning platform, developed by the Spanish company Acuratio, as a clear example of technology transfer from the laboratory to Spanish industry.

“Thanks to MLEDGE, we have been able to bring the latest research results directly to companies and clients, demonstrating that it is possible to combine efficiency, security and privacy in real time,” highlights Nikolaos Laoutaris.

MLEDGE contributes to a more secure data economy, in which sensitive information from users and companies can be reliably used to train artificial intelligence models, while optimizing the use of energy resources.

The project leaves a tangible legacy in the form of systems that can support the management of health crises, tools that optimize industrial processes, and methodologies that accelerate the adoption of federated learning in Spain. Moreover, “the collaborations established with companies such as Orange, Acuratio, and Inmarepro remain active and continue to explore new applications in areas such as smart cities, digital health, and urban mobility,” adds Laoutaris.

MLEDGE has been funded by the Spanish Ministry for Digital Transformation and the Civil Service, through the Recovery, Transformation and Resilience Plan (PRTR), with funds from the European Union – NextGenerationEU.