Privacy-Preserving Collaborative Intelligence Using Personalized Federated Learning for Heterogeneous Smart Devices
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Abstract
The increasing use of smart phones, wearable devices, home appliances, industrial sensors, and embedded intelligent systems has created a large and continuously growing source of distributed data. Although these devices can provide valuable information for machine learning applications, transferring their raw data to centralized servers raises concerns regarding privacy, communication overhead, computational cost, and data ownership. Federated learning provides an alternative by allowing devices to train models locally and share model-related information rather than their original datasets. Nevertheless, conventional federated learning can perform poorly when participating devices differ substantially in hardware capabilities, data distributions, network conditions, and application requirements. This research proposes a privacy-preserving collaborative intelligence framework based on personalized federated learning for heterogeneous smart devices. The framework separates common knowledge from device-specific knowledge through a personalized learning mechanism, enabling participating devices to benefit from collaborative training while retaining adaptation to their local characteristics. A resource-aware client selection strategy considers processing capability, battery condition, communication quality, and local dataset characteristics before initiating training rounds. To strengthen privacy protection, the framework incorporates secure aggregation and controlled update protection so that individual model contributions are not directly exposed to the coordinating server. The proposed architecture also accommodates non-identically distributed data by allowing local personalization layers to adapt the shared representation to individual device environments. This approach aims to establish a balance among privacy protection, personalization, communication efficiency, computational requirements, and predictive performance. The framework is applicable to heterogeneous smart-device environments such as intelligent homes, wearable computing, connected vehicles, industrial monitoring, and mobile health applications. The study provides a unified architectural basis for collaborative intelligence in which devices can learn collectively without requiring centralized access to their raw private data.
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