A unified threat model and evaluation framework is developed to compare privacy-preserving methods for distributed learning in IoT, showing trade-offs in privacy robustness and system efficiency with Bloom filter encodings highlighted for low overhead.
Unleashing the Power of IoT: A Comprehensive Review of IoT Applica- tions and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0
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Privacy-Preserving Distributed Learning in IoT Systems: A Unified Threat Model and Evaluation Framework
A unified threat model and evaluation framework is developed to compare privacy-preserving methods for distributed learning in IoT, showing trade-offs in privacy robustness and system efficiency with Bloom filter encodings highlighted for low overhead.