Federated unlearning (FU) algorithms allow clients in federated settings to exercise their "right to be forgotten" by removing the influence of their data from a collaboratively trained model.Existing FU methods maintain data privacy by performing unlearning locally on the client-side and sending targeted updates to the server without exposing forgotten data; yet they often rely on server-side cooperation, revealing the client's intent and identity without enforcement guarantees -compromising autonomy and unlearning privacy.In this work, we propose EFU (Enforced Federated Unlearning), a cryptographically enforced FU framework that enables clients to initiate unlearning while concealing its occurrence from the server.Specifically, EFU leverages functional encryption to bind encrypted updates to specific aggregation functions, ensuring the server can neither perform unauthorized computations nor detect or skip unlearning requests.To further mask behavioral and parameter shifts in the aggregated model, we incorporate auxiliary unlearning losses based on adversarial examples and parameter importance regularization.Extensive experiments show that EFU achieves nearrandom accuracy on forgotten data while maintaining performance comparable to full retraining across datasets and neural architectures -all while concealing unlearning intent from the server.Furthermore, we demonstrate that EFU is agnostic to the underlying unlearning algorithm, enabling secure, function-hiding, and verifiable unlearning for any client-side FU mechanism that issues targeted updates.

Parkinson’s disease (PD) is a chronic neurological condition causing an assortment of motor and cognitive prodromes. Each individual’s PD symptoms develop differently due to the variability of the ailment. This study aims to introduce the KNN Imputed Spearman’s Rank and Jaccard Convolutional Deep Neural Learning (KISRJCDNL) technique for automating early PD diagnosis depending on speech analysis. This work enhances disease diagnosis performance through preprocessing and early, precise PD detection. Several information collected from the given dataset are initially taken as input. Then, the preprocessing stage converts raw data into a structured format. Afterward, Spearman’s Rank Feature Selective and Jaccard Index–based Convolutional Deep Neural Learning Classifier with four layers, one input layer, one output layer, and two hidden layers, are deployed for diagnosing PD by efficiently performing the data classification. Experimental evaluation uses the Early Biomarkers of the PD dataset by different factors. Findings support the claim that the proposed KISRJCDNL technique enhances accuracy by 14%, reducing feature selection time, error rate, overall time, and space complexity by 16%, 43%, 36%, and 22% compared to the existing deep learning methods.

In the era of smart transportation, ensuring the reliability, safety, and security of railway systems is paramount. This paper presents an approach to anomaly detection in smart railways, leveraging self-adaptation, process mining, and digital twins. By integrating real-time data analytics with adaptive algorithms, the framework dynamically identifies and responds to anomalies, enhancing operational efficiency and safety. Extensive data from sensors and IoT devices embedded within the railway infrastructure is continuously monitored and analyzed using advanced machine learning algorithms within digital twins. These algorithms adjust their parameters in real-time to accommodate changes in the operational environment, ensuring robustness and accuracy. Process mining techniques extract valuable insights from both historical and real-time data, identifying patterns and deviations that may indicate potential anomalies. This continuous improvement loop not only detects anomalies but also understands their underlying causes, facilitating effective interventions. Digital twins provide a virtual replica of critical assets, updated in real-time, enabling proactive maintenance and predictive analysis. They also serve as a testing ground for new algorithms and strategies, ensuring risk-free evaluation before deployment. The integration of self-adaptation, process mining, and digital twins significantly improves detection accuracy and response times. Applied to railway operational scenarios, this method effectively identifies anomalies, classifying them into minor deviations or major system faults, leading to enhanced operational efficiency, reduced downtime, and improved safety for passengers and personnel. This approach has been developed within several European projects focused on data-driven innovations for smarter railways, utilizing the latest advancements in artificial intelligence and machine learning.