Privacy-Preserving Edge Personalization: Combining Local Training with Encrypted Inference
Deploying machine learning models to resource-constrained edge devices presents a fundamental engineering paradox: maximally personalized user experiences require continuous learning from private local data, yet running heavy neural networks on constrained hardware often forces offloading computations to untrusted external environments. In domains such as wearable health monitors, smart home automation, industrial sensing, and biometric authentication, sending raw sensor feeds to the cloud violates privacy regulations (such as GDPR, HIPAA, and the EU Cyber Resilience Act) and introduces unacceptable latency. Conversely, relying solely on static, pre-trained global models yields sub-optimal inference accuracy for non-stationary, user-specific data distributions.
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