Privacy-Preserving Knowledge Distillation in Latency-Critical Federated Task Offloading for Consumer IoT Networks

Along with the explosive demand for massive data computation, federated local-edge-cloud computing enables many IoT task offloading processes and has recently gained widespread attention in consumer-centric healthcare. However, existing approaches for latency-critical task offloading do not pay sufficient attention to patient privacy data and thus are far from being ready for large-scale practical deployment. In this paper, a novel local-edge-cloud based federated learning framework with one-way knowledge distillation for latency-critical and security-aware task offloading is proposed, while a task offloading scheme based on deep reinforcement learning is presented. Then, based on the consideration of privacy guarantees, computing resources and offloading decisions, an optimization problem is formulated to minimize the overall latency of all tasks for local medical devices. Additionally, a pseudo-task mechanism is adopted in the federated framework for the uploaded weights to aggregate the models on the edges and the cloud. Besides, a one-way knowledge distillation mechanism is proposed to transfer knowledge from a teacher model to student models, which compresses the network parameters and reduces the model latency. Finally, experiments based on Chest X-Ray Images of Pneumonia are conducted for corroborating the theoretical results.