In the dynamic landscape of Internet of Things (IoT), the challenge of efficiently analyzing voluminous data is paramount. This paper introduces a novel method for IoT data analysis through distributed federated learning. This approach addresses the constraints of traditional machine learning techniques, particularly in terms of scalability and privacy. By leveraging distributed computation, our method enhances data processing capabilities across a myriad of IoT devices, while ensuring data privacy and reducing bandwidth requirements. The efficacy of this approach is demonstrated through its application in optimizing energy consumption patterns in smart home environments.