FedWAvg: Mitigating Model Contamination in UAV Networks through Federated Weighted Average for Weather Forecasting

Balavardhan Reddy Konda, Veera Manikantha Rayudu Tummala, Sai Kumar Reddy Ganugapenta, Praveen Kumar Siraparapu, Abhishek Hazra, Mohan Gurusamy

Abstract: Unmanned Aerial Vehicles (UAVs) are like modern weather surveyors, flying through the skies and collecting valuable atmospheric data with their instrumentation. However, collecting accurate timing data involves a delicate balance between energy conservation and high-speed operation across a variety of computing devices. In order to address this issue, we created a unique Federated Learning computer system designed for the development of weather forecasting using data collected from UAVs. This dynamic variation now reduces operational completion instances through outlier detection and a softmax weight allocation. In the long run, the weather forecast’s overall performance and effectiveness have been greatly improved. A splendid innovation in our framework is creating Federated Weighted Average (FedWAvg) set of rules, specifically designed to deal with delays due to outliers or inaccuracies at some stage in the discussion. FedWAvg allows quick convergence without compromising statistical accuracy, increasing the trustworthiness of weather forecasting in real-world conditions. Putting those advancements together, we have made significant improvements to make weather forecasting more reliable and useful, for the people and the businesses that rely on weather forecasting to get better rewards.