A Federated Cascade Learning Approach for Efficient Occupancy Detection in Smart Buildings

Occupancy detection plays a pivotal role in optimizing energy consumption, enhancing occupant comfort, and enabling intelligent decision-making in smart building environments. While traditional centralized learning methods often compromise user privacy and scalability, federated learning (FL) offers a promising privacy-preserving alternative. In this study, we propose Federated Cascade Learning with Long Short-Term Memory and Attention (FCL-LSTMA), a novel decentralized architecture that synergistically combines FL and cascade learning to enable accurate and efficient occupancy detection across distributed environments. The FCL-LSTMA framework operates through a two-stage training process: initially, Block B1 leverages 1D convolution, batch normalization, and a parallel LSTM layer for foundational feature learning. Once stabilized, the block is frozen and extended to Block B2, which incorporates attention mechanisms and an additional LSTM layer to refine temporal feature extraction. This progressive training ensures improved stability, model depth, and generalization without compromising data privacy. Experimental results on four benchmark datasets demonstrate the superiority of the proposed framework. FCL-LSTMA achieves outstanding accuracy for binary occupancy detection: 99.99% on the Living Room dataset, 98.95% on the UCI Occupancy Detection dataset, 99.95% on both the UCI Room Occupancy Estimation and IEQ datasets. For multi-class tasks, the model achieves 99.98% and 99.95% on the Living Room and UCI Room Occupancy Estimation datasets, respectively. Furthermore, it demonstrates computational efficiency with inference times ranging from 0.2028 to 0.562 milliseconds and memory usage between 0.17 MB and 0.49 MB. These results confirm the robustness, scalability, and privacy-preserving capabilities of FCL-LSTMA, making it a compelling solution for real-time occupancy detection in smart building systems.