This post - print is the paper version that was submitted to ZINC 202 5 . Abstract -- Recently, federated learning frameworks such as Python TestBed for Federated Learning Algorithms and MicroPython TestBed for Federated Learning Algorithms have emerged to tackle user privacy concerns and efficiency in embedded systems. Even more recently, an efficient federated anomaly detection algorithm, FLiForest, based on Isolation Forests has been developed, offering a low - resource, unsupervised method well - suited for edge deployment and continuous learning. In this paper, we present an appli cation of Isolation Forest - based temperature anomaly detection, developed using the previously mentioned federated learning frameworks, aimed at small edge devices and IoT systems running MicroPython. The system has been experimentally evaluated, achieving over 9 6 % accuracy in distinguishing normal from abnormal readings and above 78 % precision in detecting anomalies across all tested configurations, while maintaining a memory usage below 16 0 KB during model training.

ROC or Receiver Operating Characteristic Curve is the most frequently used tool for evaluating the binary or multi-class classification model. Unlike other metrics, it is calculated on prediction scores like Precision-Recall Curve instead of prediction class. In my previous post, the importance of the precision-recall curve is highlighted as how to plot for multi-class classification. To understand ROC Curve, let's quickly refresh our memory on the possible outcomes in a binary classification problem by referring to the Confusion Matrix. ROC Curve is a plot of True Positive Rate(TPR) plotted against False Positive Rate(FPR) at various threshold values. It helps to visualize how threshold affects classifier performance.

In some of my previous blogs I have discussed different machine learning algorithms. Using those algorithms we can build our models. We do data cleansing, pre-processing and then pass the data into our model. The model does the prediction. How to know if the model is good or bad.

ROC (Receiver Operating Characteristics) -- It was originated from signal detection theory. Now it is heavily used by Data Miners, Economists and in Machine Learning. It basically shows the trade-off between the true positive rate (TPR) and false positive rate (FPR). So we compare the actual Vs predicted and find the TPR and FPR and plot a graph for all the data points (below). In this case the curve which we get is called ROC.

Think of a regression model mapping a number of features onto a real number (potentially a probability). The resulting real number can then be mapped on one of two classes, depending on whether this predicted number is greater or lower than some choosable threshold. Let's take for example a logistic regression and data on the survivorship of the Titanic accident to introduce the relevant concepts which will lead naturally to the ROC (Receiver Operating Characteristic) and its AUC or AUROC (Area Under ROC Curve). Every record in the data set represents a passenger – providing information on her/his age, gender, class, number of siblings/spouses aboard (sibsp), number of parents/children aboard (parch) and, of course, whether s/he survived the accident. The logistic regression model is tested on batches of 10 cases with a model trained on the remaining N-10 cases – the test batches form a partition of the data. In short, Leave-10-out CV has been applied to arrive at more accurate estimation of the out-of-sample error rates.