## 6 Conclusions  
In this paper, we present a low-cost and power-efficient solution for retrofitting the existing mechanical-based metering infrastructure. This solution is based on a low-power MCU platform utilizing NB-IoT network technology for communication between edge devices and the server. The device is fitted with a low-cost camera that produces images of the metering device, resulting in datasets consisting of 3500 six-digit images. Batches of 150 images are labeled by the high-level entity learning model and preprocessed in order to remove unwanted artifacts and convert them to black-and-white format. Reduction from the original image size of 640 \(\times\) 480 pixels to 16 \(\times\) 24 format brings a reduction of input instance size by 99.87%. Furthermore, conversion from 8-bit grayscale to black-and-white bitmap brings an additional eight-fold reduction in size, totaling 48 bytes per image, as opposed to the 300 KB provided by the camera. The resulting digit image datasets are used to train models on each device using a custom-tailored CNN architecture optimized for our system. We devised two test cases for testing. The first test case consists of two scenarios, where the first one performs two subsequent dataset generation and model training iterations. On the other hand, the second scenario is enhanced by the introduction of FL based on model averaging methodology after each training iteration on the device. The two scenarios showed the advantage of using FL, where the model obtained by the combination of two training iterations and intermediate FL averaging produces better average accuracy than the model obtained by only two iterations of training. To conclude, the resulting average accuracy of models generated using FL is 93.6%. The second test case demonstrates the influence of FL on a system comprising two batches of devices deployed at different times. The first batch undergoes training with FL, as described in Scenario 2 of test case 1. However, the initialization of the second batch, which is based on the global model generated from training on Batch 1, shows a notable impact, with faster improvements in accuracy for Batch 2. Additionally, further training on devices in Batch 2 significantly affects the models in Batch 1, as the FL update \"shares\" new knowledge, leading to a substantial accuracy increase in Batch 1. Ultimately, the highest accuracy of 97.01% is achieved following the final FL update, representing the best overall performance. The choice of low-end components and hardware is justified by the power-efficient design of the edge device, making it capable of operating for more than a year in an extensive use case and even more when the frequency of readings is reduced. This is extremely important for the feasibility and scalability of the system in a real environment. Usage of a power-hungry high-end device would impose a huge maintenance problem associated with the need for daily recharging/replacement of batteries for thousands of devices while not bringing any significant boost in the performance. Current Limitations, Real-World Application, and Future Work  
This paper presents the results achieved after completing the \"Proof of Concept\" project titled \"Federated Learning-Based Number Recognition for Smart Metering Applications Using NB-IoT Wireless Technology\". The results demonstrate that the proposed solution could be applied in real-world metering scenarios. Currently, our solution works well if there is a signal from the MNO (either using NB-IoT or falling back to GSM). If there is no MNO signal in the region, a possible and viable solution would be to redesign our system to work with LoRa/LoRaWAN. However, the cost of such a versatile solution would be significantly higher. For a real-world application, it is necessary to consider several additional aspects. Most mechanical meters are located underground (e.g., in manholes) or in cabinets behind concrete walls, where radio conditions are poor.