Communication-Efficient Federated Learning in Drone-Assisted IoT Networks: Path Planning and Enhanced Knowledge Distillation Techniques

As 5G and beyond networks continue to proliferate, intelligent monitoring systems are becoming increasingly prevalent. However, geographically isolated regions with sparse populations still face difficulties in accessing these technologies due to infrastructure deployment challenges. Additionally, the high cost and unreliability of satellite Internet services make them less appealing. This paper studies the challenges of droneaided networks and presents a communication-efficient Federated Learning (FL) system on a drone-aided Internet of Things (IoT) network to facilitate health analysis in rural areas over LoRa wireless links. The proposed approach consists of two primary components. Firstly, optimizing the drone's trajectory is theoretically formulated as a modified version of the Traveling Salesman Problem (TSP), with the Self-Organizing Map (SOM) algorithm employed for effective route planning. Secondly, the Knowledge Distillation (KD)-based FL algorithm is utilized to reduce communication overhead by leveraging soft labels. The quality of drone routes generated by the SOM is evaluated on multi-scale maps with pre-determined optimal paths. The experiments reveal SOM's ability to accurately represent node topologies and yield cost-effective Hamiltonian cycles. The KD-based FL proves to be more efficient in terms of communication than FedAvg as the former exchanges soft labels while the latter exchanges model weights, thus reducing drone waiting time and battery consumption. We showcase the performance of our KD-based FL algorithm using Human Activity Recognition (HAR) datasets, illustrating a communication-efficient alternative for distributed learning, offering competitive performance leveraging a shared dataset for knowledge transfer among IoT devices.