Federated Learning (FL) has emerged as a promising privacy-preserving approach for training Machine Learning (ML) models in industries handling sensitive data, such as healthcare. However, the practical application of FL in healthcare faces challenges due to complex privacy regulations and the lack of inherent robust governance mechanisms to ensure compliance and protect data ownership. To address these limitations, we propose a governed FL framework that leverages the Eclipse Dataspace Components (EDC) connector to enforce compliance and data sovereignty through the implementation of data access and usage policies. We demonstrate the effectiveness of our approach by training an in-hospital mortality prediction model and an Alzheimer’s Disease (AD) detection model using FL with Federated Averaging (FedAvg) and Secure Aggregation (SecAgg). Our results demonstrate that the governed FL framework enables the training of models with performance comparable to those trained on centrally aggregated data. At the same time, it effectively introduces governance through policy enforcement, making it a practical and compliant solution for healthcare applications.

In the evolution of optical networks, spectral efficiency (SE) enhancement has been the most cost-efficient and thus the main driver for capacity increase for decades. As a result, the development of optical transport systems has been focused on the$C$- and$L$-bands, where silica optical fiber exhibits the lowest attenuation, and erbium-doped fiber amplifiers provide an efficient solution to compensate for the optical loss. With a gradual maturity in the SE growth, however, the extension of the optical bandwidth beyond the$C + L$-band is expected to play a significant role in future capacity upgrades of optical networks and, thus, attract increasing research interests. In this article, we discuss the merits and challenges of ultrawideband optical transport systems and networks beyond conventional bands.

This paper reports on a field trial in a robotic manufacturing cell. Channel measurements and transmission experiments were conducted, assisting the development of a light-emitting diode-based optical wireless communication (OWC) system tailored for the needs of industrial wireless applications. These are moderate data rates, reliability, and low latency. Since dedicated radio spectrum is hardly available, the usage of light is an interesting alternative. Due to its spatial confinement, OWC links provide enhanced security and are difficult to jam from outside a building-a particularly interesting property for industrial applications. We performed broadband distributed $8\times 6$ multiple-input multiple-output (MIMO) channel measurements in a manufacturing cell. Results confirm that the signal-to-noise ratio is sufficient if a line-of-sight (LOS) is available and bandwidth is limited to a few megahertz. However, small movements and rotations may lead to sudden fades of 10-20 dB, when an LOS is blocked. To improve reliability, antenna diversity concepts are applied. Results show that for reliable communication, MIMO diversity schemes are indispensable as it increases the probability of a free LOS significantly. Based on these insights, we suggest an OWC design tailored to industrial wireless applications.