Résumé: Edge computing and multi-access edge computing (MEC) are two recent paradigms of distributed computing that are growing due to the rise of the fifth-generation (5G) of broadband cellular networks. The development of edge computing and MEC architectures involves the hosting of applications close to the end-users, allowing: an improved privacy, given that critical data is not shared with other systems; a reduced communication latency; an improved application speed; and a more efficient energy use. However, many applications are challenged by edge computing and MEC. In the case of machine learning (ML) applications, there can be privacy rules that do not allow data to be shared among distinct edges. Additionally, the devices used to train ML models might present lower computational capabilities than traditional computers. In this work, we present a Federated ML architecture that uses decentralized data and light ML training techniques to fit ML models on the 5G Edge. Our system consists of edge nodes that train models using local data and a centralized node that aggregates the results. As a case study, an international revenue share fraud task is addressed by considering two real-world datasets obtained from a commercial provider of Telecom analytics solutions. We test our architecture using two iterations of a Federated ML method, then compare it with a centralized ML model that is currently adopted by the provider. The results show that the Federated Learning decentralized approach produces an excellent level of class discrimination and that the main models maintain the performance across two rounds of decentralized training and even surpass the existing centralized model. After validating the results with the Telecom provider, we have built a prototype technological architecture that can be deployed in a real-world MEC scenario.

Langue: Anglais