Chetan More

The emerging 6G technologies will bring billions of IoT sensors which are going to demand little radio spectrum. The resulting high level of connectivity poses some novel problems, among them network congestion, and privacy: because energy-consumption data might be shared unintentionally, it provides insight into personal behavioral patterns. In addition, the disproportionate access to highly developed infrastructure can contribute to the need to continue the digital divide between urban and rural areas. This paper presents a decentralized infrastructure combining federated learning and Hyperledger Fabric to deal with these issues. To implement it, PyTorch is used to perform distributed learning tasks, and MATLAB is used to generate the synthetic spectrum traces that reflect the real-world CBRS conditions based on the NTIA field measurements between 2021 and 2024. These traces were validated by Kolmogorov–Smirnov test ( p = 0.87). The framework outperforms conventional centralized DSS benchmarks while complying with 3GPP Release 18 transparency and ETSI ESG principles. The framework is a modular, open-source system with sharding that scales horizontally to over 5,000 nodes. The proposed solution successfully addresses the scarcity of the spectrum with the use of cooperative resource sharing through congested urban vehicle-to-everything (V2X) networks, remote solar-powered microgrids, and so on. The findings verify that the next generation 6G networks are capable of not only providing extremely high data rates but also improved privacy and fair connectivity in different environments.

Lung cancer is a major problem because it kills more people every year than any other type of cancer. If we can find the abnormal areas early, people are much more likely to recover. Doctors usually use a type of scan called a CT scan to look for these abnormal areas. The problem is that reviewing all the scan images is very tedious, and even experienced doctors can miss something. We wanted to see whether computers could help with this task. We tried using three kinds of computer programs, VGG16, ResNet50 and DenseNet121, to look at the pictures from the scan. We used a collection of pictures called the LIDC-IDRI collection to teach the computers what to look for. DenseNet121 performed best at identifying the regions. We think this is because it is good at using the information it has to make decisions. We also used a tool called Grad-CAM to see what the computers were actually looking at. It showed that the better programs focused on the abnormal areas, not just the normal tissue. Our results show that using computers to help with this task is promising, and it could help doctors make better decisions. We also know there is still a lot of work to do to make it perfect.