Accurate energy consumption forecasting is critical for optimizing energy usage, lowering operational costs, and encouraging sustainability in smart buildings. Machine learning (ML) has developed as an effective method for energy forecasting, using sensor data to anticipate consumption trends and increase efficiency. However, due to regulations such as GDPR and growing privacy concerns, sharing sensitive energy data with third parties is often prohibited, providing issues for traditional centralized ML techniques. Federated Learning (FL) provides a feasible alternative by allowing for decentralized model training across several buildings without explicitly exchanging raw data. This privacy-preserving strategy enables organizations to jointly train reliable models while retaining data sovereignty. Our experimental results demonstrate that by using the CU-BEMS dataset, both FL and centralized forecasting models perform similarly, with an R2 score of ≈ 87%. Furthermore, FL decreases bandwidth use by limiting data transfers, making it a scalable and economical energy management solution for smart buildings. These findings demonstrate FL's ability to ensure safe, data-driven decision-making for sustainable energy utilization