Trust-Based Anomaly Detection in Federated Edge Learning

We present a novel approach for anomaly detection in a decentralized federated learning setting for edge units. We propose quantifiable metrics of Reputation and Trust that allow us to detect training anomalies on the local edge units during the learning rounds. Our approach can be combined with any aggregation method used on the server and does not impact the performance of the aggregation algorithm. Moreover, our approach allows to perform an audit of the training process of the participating edge units across training rounds based on our proposed metrics. We verify our approach in two distinct use cases: financial applications with the objective to detect anomalous transactions, and Intelligent Transportation System supposed to classify the input images. Our results confirm that our approach is capable of detecting training anomalies and even improving the effectiveness of the learning process if the anomalous edge units are excluded from the training process.