Privacy in Iot: Federated Learning AND Telemetry Anonymization

The article examines privacy issues in IoT systems and approaches for addressing them through federated learning and telemetry anonymization methods. It provides a detailed overview of the federated learning (FL) approach, its architecture, and the mathematical optimization framework that enables models to be trained on distributed devices without transferring raw data [1][2]. Classical anonymization techniques (pseudonymization, k-anonymity, L-diversity) and differential privacy with the corresponding formulas are described [3][4]. Practical application cases are considered: in healthcare, FL enables joint model training on medical data without violating patient privacy [5]; in smart cities, IoT devices (e.g., smartphones and sensors) can train models locally (speech recognition, traffic prediction) and transmit only updates [6]; in the energy sector, regional networks collaboratively build consumption prediction models [6]. Schematic illustrations and tables are presented (e.g., distributed learning architecture in Fig. 1 and a comparison of privacy methods in Table 1).

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