P3FL: A Privacy-Preserving Personalized Federated Learning Framework for Collaborative Smart Home Predictions and Decision-Making

Ye Wang, Hansong Xu, Kun Hua, Yang Bai, Jianqi Yu, Wenyin Zhu, Lixing Chen, Bo Yang, Xinping Guan

Abstract: Smart homes depend on collaborative sequential prediction tasks to optimize energy consumption and appliance scheduling. Federated learning (FL) offers a promising approach by enabling decentralized model training to balance privacy and usability. Yet, standard FL techniques fail to effectively address data diversity and individual user preferences in smart home contexts. To address these issues, we propose privacy-preserving personalized FL (P3FL): a P3FL framework that integrates tailored model training and privacy enhancements for federated collaborative predictions and decision-making. Our framework introduces the personalized collaborative decision-making (PCDM) algorithm, which dynamically adapts to different household environments while ensuring privacy and personalization. P3FL combines a global model for knowledge aggregation with a personalized adaptation module to provide fine-tuned predictions based on user preferences, environmental factors, and device configurations. Theoretical convergence bounds analysis confirms the robustness and efficiency of PCDM under conditions of strong convexity, smoothness, and bounded variance. Extensive experiments on real-world smart home datasets demonstrate that P3FL outperforms state-of-the-art methods, with PCDM achieving a training accuracy of 92.14%. Our approach enhances operational efficiency and ensures personalized user satisfaction, privacy enhancement in smart homes.

External IDs:dblp:journals/iotj/WangXHBYZCYG25