Parkinson’s Disease is the second most prevalent neurodegenerative disorder, currently affecting as high as 3% of the global population. Research suggests that up to 80% of patients manifest phonatory symptoms as early signs of the disease. In this respect, various systems have been developed that identify high risk patients by analyzing their speech using recordings obtained from natural dialogues and reading tasks conducted in clinical settings. However, most of them are centralized models, where training and inference take place on a single machine, raising concerns about data privacy and scalability. To address these issues, the current study migrates an existing, state-of-the-art centralized approach to the concept of federated learning, where the model is trained in multiple independent sessions on different machines, each with its own dataset. Therefore, the main objective is to establish a proof of concept for federated learning in this domain, demonstrating its effectiveness and viability. Moreover, the study aims to overcome challenges associated with centralized machine learning models while promoting collaborative and privacy-preserving model training.

5G is the latest wireless standard designed to comply with stringent performance requirements for supporting diverse use cases spanning over different verticals, considered not feasible with previous cellular network technology. In this context, the European Commission has funded multiple research projects aiming at validating 5G Key Performance Indicators (KPIs) over large scale field trials on different use cases. The 5GENESIS project provides a flexible and open experimentation suite with network slicing, in order to support and facilitate validation of KPIs over 5G infrastructure. This paper discusses the experimentation framework of the Athens 5GENESIS Facility, presents results on throughput, Round-Trip-Time (RTT) and latency and summarizes the lessons learned from the activities that took place.

Recently Unmanned Aerial Vehicles (UAVs) have evolved considerably towards real world applications, going beyond entertaining activities and use. With the advent of Fifth Generation (5G) cellular networks and the number of UAVs to be increased significantly, it is created the opportunity for UAVs to participate in the realisation of 5G opportunistic networks by carrying 5G Base-Stations to under-served areas, allowing the provision of bandwidth demanding services, such as Ultra High Definition (UHD) video streaming, as well as other multimedia services. Among the various improvements that will drive this evolution of UAVs, energy efficiency is considered of primary importance since will prolong the flight time and will extend the mission territory. Although this problem has been studied in the literature as an offline resource optimisation problem, the diverse conditions of a real UAV flight does not allow any of the existing offline optimisation models to be applied in real flight conditions. To this end, this paper discusses the amalgamation of UAVs and 5G cellular networks as an auspicious solution for realising energy efficiency of UAVs by offloading at the edge of the network the Flight Control System (FCS), which will allow the optimisation of the UAV energy resources by processing in real time the flight data that have been collected by onboard sensors. By exploiting the Multi-access Edge Computing (MEC) architectural feature of 5G as a technology enabler for realising this offloading, the paper presents a proof-of-concept implementation of such a 5G-enabled UAV with softwarized FCS component at the edge of the 5G network (i.e., the MEC), allowing by this way the autonomous flight of the UAV over the 5G network by following control commands mandated by the FCS that has been deployed at the MEC. This proof-of-concept 5G-enabled UAV can support the execution of real-time resource optimisation techniques, a step-forward from the currently offline-ones, enabling in the future the execution of energy-efficient and advanced missions.

5G introduces new paradigms that enable communication networks to become more flexible and handle a wide variety of network services from various vertical domains. Small cell deployment exploiting virtualization techniques as envisaged by 5G, allows enhanced mobile edge computing capabilities, thus enabling flexible network service deployment and slicing support near the end user. This paper presents a cloud-enabled small cell architecture for 5G networks developed within the 5G-ESSENCE project. The paper also presents the implementation and deployment of an Ethernet Virtual Connection service over a 5G-enabled infrastructure.