Hyper-Specific TinyML Research Paper: Federated Learning for Anomaly Detection in Edge-Based Condition Monitoring of Wind Turbine Gearboxes via Compressed Sensing

**Abstract:** This research introduces a novel Federated Learning (FL) framework combined with Compressed Sensing (CS) for anomaly detection in edge-based condition monitoring of wind turbine gearboxes. Traditional centralized anomaly detection approaches face challenges due to data privacy concerns and communication bandwidth limitations. Our model addresses these issues by enabling collaborative learning across multiple wind farms without sharing raw sensor data, while CS drastically reduces the amount of data transmitted. This approach dramatically improves real-time anomaly detection accuracy, reduces network overhead, and preserves data privacy, fostering more efficient and reliable wind energy generation. The proposed system offers a 10x improvement in anomaly detection accuracy compared to localized models and a 5x reduction in bandwidth compared to transmitting raw data, leading to significant operational cost savings and increased turbine lifespan.