# FL-LoRaMAC: A Novel Framework for Enabling On-Device Learning for LoRa-Based IoT Applications
## 2 Related Work  
In this section, we explore real-world applications of machine learning, along with the methods and algorithms employed. Furthermore, we delve into the ongoing research and collaboration between IoT and AI in order to shed light on the existing work in this field. The utilization of machine learning algorithms for the detection of oil spills in radar images is described in [15], which presents an in-depth examination of the challenges intertwined with this application. The study also proposes novel strategies for effectively tackling these obstacles. To substantiate their claims, the authors conduct comprehensive experimental analyses that specifically focus on two pivotal aspects of machine learning: batched and imbalanced training sets. Within this context, the paper introduces two novel algorithms, namely SHRINK and one-sided selection, that demonstrate remarkable efficacy in controlling the false alarm rate. Hence, this paper not only sheds light on the practical implementation of machine learning techniques for oil spill detection but also provides solutions to overcome the associated challenges. The application of support vector machines, decision trees, and classification methods for detecting land cover changes and mapping in rural areas is presented in [16]. Three objectives were achieved: identifying suitable bands for classification, comparing the performance of the methods, and detecting changes in land cover. Data preprocessing was performed using ERDAS IMAGINE 9.1 and ENVI 4.5. The results show that the decision tree algorithm outperformed the other methods in terms of accuracy and performance. Failure degradation was also estimated, providing insights into method limitations. The paper highlights the effectiveness of decision trees for land cover change detection in rural areas and offers guidance for future improvements. The use of support vector machines (SVMs) as a regression technique for estimating soil moisture with remote sensing data was explored in [17]. Ten sites in the western United States were studied. SVMs outperformed multiple linear regression (MLR) and artificial neural networks (ANNs) in accuracy and performance. The findings contribute to improved soil moisture estimation and management in the studied regions. Human health benefits much from the advent of IoT and AI tools. In [18], the authors proposed a highly reliable method for measuring blood pressure without the need of putting cuffs. The proposed model uses a combination of information from the ECG signal and PPG to measure blood pressure. The study first establishes a relationship between blood pressure and the ECG. This enables the user with the ECG sensor to potentially measure blood pressure without requiring any additional device. The authors of [19] proposed a novel solution for predicting heart diseases using the cascaded deep learning model in the fog computing environment. Their solution makes use of data from multiple sensors that provide data about daily activity that are fed to an ensemble classifier. The classifier is hosted in the fog environment and computations are performed in a decentralized manner. The proposed approach achieved a prediction accuracy of 95%. A transfer learning methodology used to decrease the computational resources needed to train a deep neural network for a reinforcement learning (RL) problem was presented in [16]. The deep neural network is trained on a varied set of meta-environments to acquire broad domain knowledge, which can then be transferred to test environments, and only the last few fully connected layers are trained. The performance of the algorithm is evaluated in terms of mean safe flight, and it was observed that the network's performance is comparable to that of training the network end-to-end, while significantly decreasing the latency and energy consumption by 1.8 and 3.7 times, respectively. The authors of [20] proposed an ECG classification system that makes use of LoRa and fog computing.