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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

Radio Resource Management (RRM) is especially crucial for wireless multimedia networking, where: (i) wireless resources are scarce and precious; (ii) multimedia applications impose a strong Quality of Service (QoS) support. In this context, Mobile Network Operators (MNO) have to find a trade-off between optimizing wireless resources (in terms of radio utilization), while ensuring QoS and hence increasing user satisfaction (or Quality of Experience - QoE). Traditional ways of managing radio resources, using information from monitoring technical parameters, fail to give accurate evaluations of user experience. Particularly, multimedia traffic should not be measured only in terms of throughput, loss rate, or delay but rather in terms of user experience. In this paper, we fill this gap by introducing a new paradigm for radio resource management (or RRM) based on QoE. We focus on the management of wireless environments at network side and without modification on user terminal. Further, we present several examples of user's case to demonstrate how MNO can use QoE to handle three important issues related to RRM, namely access (admission) control, physical rate adaptation, and packet scheduling in wireless environment. The obtained results are encouraging and open a wide range of research directions. Finally, this paper also provides a review on recent patents in the field of Quality of Experience over wireless networks.