Edge Reinforced Learning Platform with Homomorphic Encryption and Swarm Intelligence for Ultra-Low Latency IoT Sensing and Cross-Device Communication

This paper presents an edge-reinforced learning platform that combines reinforcement learning, homomorphic encryption, and swarm intelligence to support ultra-low latency IoT sensing and cross-device communication. In conventional IoT architectures, cloud-centric processing and centralized coordination introduce significant delays and expose sensitive data to intermediate entities, making them unsuitable for time-critical and privacy-sensitive applications. The proposed platform relocates intelligence to the network edge, where edge nodes learn adaptive policies for sensing, routing, and computation offloading based on local conditions and limited global feedback. To preserve confidentiality, IoT measurements and model updates are protected using homomorphic encryption, allowing aggregation and decision-making to be performed directly over encrypted data without revealing raw values. In parallel, swarm intelligence mechanisms orchestrate distributed cooperation among devices, enabling robust path selection, task allocation, and congestion avoidance through lightweight, bio‑inspired interactions rather than centralized control. The integrated design is evaluated on realistic IoT scenarios with heterogeneous devices and dynamic traffic patterns. Results show that the edge-reinforced learning platform can significantly reduce end-to-end latency and jitter compared to cloud-based and non-learning edge baselines, while incurring acceptable computational overhead from encryption and maintaining strong privacy guarantees. The framework demonstrates that it is feasible to simultaneously achieve low latency, resilient cross-device coordination, and data confidentiality in large-scale IoT deployments.