AI‐Enabled 6G Space‐Air‐Ground–Integrated Networks for Ultra‐Reliable Low Latency Internet of Medical Things Healthcare
TL;DR
An AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things applications and a multi agent—deep deterministic policy gradient (MA‐DDPG) agent that allocates resources in a distributed manner and a hierarchical federated learning (HFL) system with delay‐sensitive aggregation to train models privately.
Abstract
This paper explores an AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things (IoMT) applications. The generated healthcare data by the IoMT devices are processed by three levels in the considered scenario including the LEO satellites, the UAV swarms, and the ground MEC servers and adhere to strict latency, reliability, and privacy requirements. We aim at designing a multi‐tier resource allocation policy and federated learning policy that coordinates end‐to‐end latency and energy consumption and at the same time is highly accurate in terms of the model given privacy constraints. In this direction, we come up with a multi agent—deep deterministic policy gradient (MA‐DDPG) agent that allocates resources in a distributed manner and a hierarchical federated learning (HFL) system with delay‐sensitive aggregation to train models privately. Extensive simulation findings indicate that the presented framework can achieve 4.2 ms latency, 99.92% reliability, and 96.2% federated (global) model accuracy and 67% minimization of communication overhead, all of which are superior to baseline and the state‐of‐the‐art approaches in a variety of measures.