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Increasing number of ubiquitous devices are being used in the medical field to collect patient information. Those connected sensors can potentially be exploited by third parties who want to misuse personal information and compromise the security, which could ultimately result even in patient death. This paper addresses the security concerns in eHealth networks and suggests a new approach to dealing with anomalies. In particular we propose a concept for safe in-hospital learning from internet of health things (IoHT) device data while securing the network traffic with a collaboratively trained anomaly detection system using federated learning. That way, real time traffic anomaly detection is achieved, while maintaining collaboration between hospitals and keeping local data secure and private. Since not only the network metadata, but also the actual medical data is relevant to anomaly detection, we propose to use differential privacy (DP) for providing formal guarantees of the privacy spending accumulated during the federated learning.

One of the key challenges in cognitive radio is the problem of spectrum sharing. All sharing techniques proposed in the literature try to utilize the spectrum in the most efficient way, but they either lack on computational simplicity or on spectral efficiency. Network MIMO can be seen as a very promising method for spectrum sharing, because it enables multiple systems to use the same spectrum simultaneously, but requires high processing power. This paper proposes a novel cooperative technique for spectrum sharing between multiple secondary cognitive radio systems. The method, named Network Coordinated Beamforming with user Clustering (NCBC), is based on Network MIMO and utilizes Network Coordinated Beamforming (NCBF) and User Clustering (UC). The proposed NCBC approach decreases the computational complexity of NCBF making it suitable for real time spectrum sharing in cognitive radio. Monte Carlo simulations are carried out to examine the performance of the proposed technique in terms of Signal to Interference Ratio (SIR) and channel correlation. The results depict the superior performance of NCBC compared to a simple Frequency Division Spectrum Sharing (FDSS) technique in terms of aggregate system capacity and sum rate.