Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known asconcept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenario.

A simple and fast technique that allows a diagnosis of faulty elements in antenna arrays, that only needs to consider a small number of samples of its degraded far-field pattern is described. The method tabulates patterns radiated by the array with 1 faulty element only. Then the pattern corresponding to the configuration of failed/unfailed elements under test is calculated using the error-free pattern and the patterns with 1 faulty element. The configuration with the lowest difference between the calculated and the degraded patterns is selected. Comparison of the performance of this method using an exhaustive search and a genetic algorithm for an equispaced linear array of 100 lambda/2-dipoles is shown. Mutual coupling as well as noise/measurement errors in the pattern samples was considered in the numerical analysis.