Publisher Copyright: © 2026 IEEE. EC/HE/101071179/EU//SUSTAIN EC/HE/101099491/EU//HOLDEN

The increasing cloudification and softwarization of networks foster the interplay among multiple independently managed deployments. An appealing reason for such an interplay lies in distributed Machine Learning (ML), which allows the creation of robust ML models by leveraging collective intelligence and computational power. In this paper, we study the application of the two cornerstones of distributed learning, namely Federated Learning (FL) and Knowledge Distillation (KD), on the Wi-Fi Access Point (AP) load prediction use case. The analysis conducted in this paper is done on a dataset that contains real measurements from a large Wi-Fi campus network, which we use to train the ML model under study based on different strategies. Performance evaluation includes relevant aspects for the suitability of distributed learning operation in real use cases, including the predictive performance, the associated communication overheads, or the energy consumption. In particular, we prove that distributed learning can improve the predictive accuracy centralized ML solutions by up to 93% while reducing the communication overheads and the energy cost by 80%.

We present Tesla-Rapture, a gesture recognition system for sparse point clouds generated by mmWave Radars. State of the art gesture recognition models are either too resource consuming or not sufficiently accurate for the integration into real-life scenarios using wearable or constrained equipment such as IoT devices (e.g., Raspberry PI), XR hardware (e.g., HoloLens), or smart-phones. To tackle this issue, we have developed Tesla, a Message Passing Neural Network (MPNN) graph convolution approach for mmWave radar point clouds. The model outperforms the state of the art on three datasets in terms of accuracy while reducing the computational complexity and, hence, the execution time. In particular, the approach, is able to predict a gesture almost 8 times faster than the most accurate competitor. Our performance evaluation in different scenarios (environments, angles, distances) shows that Tesla generalizes well and improves the accuracy up to 20% in challenging scenarios, such as a through-wall setting and sensing at extreme angles. Utilizing Tesla, we develop Tesla-Rapture, a real-time implementation using a mmWave Radar on a Raspberry PI 4 and evaluate its accuracy and time-complexity. We also publish the source code, the trained models, and the implementation of the model for embedded devices.

We address the realization of the Findability, Accessibility, Interoperability, and Reusability (FAIR) data principles in an Internet of Things (IoT) application through a data transfer protocol. In particular, we propose an architecture for the Message Queuing Telemetry Transport (MQTT) protocol that validates, normalizes, and filters the incoming messages based on the FAIR principles to improve the interoperability and reusability of data. We show that our approach can significantly increase the degree of FAIRness of the system by evaluating the architecture using existing maturity indicators in the literature. The proposed architecture successfully passes 18 maturity indicators out of 22. We also evaluate the performance of the system in 4 different settings in terms the latency and dropped messages in a simulation environment. We demonstrate that the architecture not only improves the degree of FAIRness of the system but also reduces the dropped messages rate.