FedLoRa: IoT Spectrum Sensing Through Fast and Energy-Efficient Federated Learning in LoRa Networks

Abstract

Effective spectrum sensing is quintessential to decrease spectrum congestion across time, space and frequency in Internet of Things (IoT) networks. To circumvent the severe bandwidth constraints of IoT networks, federated machine learning (FML) can be used, but it is still unclear whether FML can be successfully performed in resource-constrained bandwidth-limited IoT networks. In this paper, we demonstrate for the first time that FML can tolerate losses up to a certain percentage and still converge. Then, we leverage this key result to design FedLoRa, an optimization networks for LoRa that is (i) fast, as it reduces the FML round time in comparison with other resource allocation schemes; (ii) energy-efficient, as the time reduction does not imply a higher energy consumption. The key idea is to balance the network load over the available spreading factors, and to exploit sequential polling of nodes to maximize the number of simultaneous non-interfering transmissions, leading to a shorter FML round time. As the problem is NP-Hard, we provide an approximation algorithm. We evaluate the performance of FedLoRa through experimental evaluation on the Colosseum channel emulator, as well as with real-world data collection with off-the-shelf LoRa devices in an 5kmx5km urban setting in Portland, Maine. Our results show that FedLoRa reduces the round time by up to about 35%, as compared to the baselines.