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Neighbor Collaboration-Based Secure Federated QoS Prediction for Smart Home Services

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Services Computing – SCC 2022 (SCC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13738))

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Abstract

Smart homes IoT (SHIoT) deploys various devices that can achieve rich functionality by invoking cloud services for better in-home experiences. Recommending the quality services will allow the devices to provide greater comfort to the homeowner. Traditional service recommendation methods require collecting the quality of service (QoS) data to achieve better recommendations but also pose privacy problems. Inspired by federated learning (FL), some privacy-preserving federated recommendation methods have been proposed. However, studies have demonstrated that user preferences and even raw data can be inferred in FL. To implement service recommendations in SHIoT while protecting the edge privacy, we propose a secure federated QoS prediction method with neighbor collaboration (NCSF). By collaboration, users in NCSF upload perturbed updates, while the perturbations are offset during server aggregation without affecting the global model. Experiments on real-world datasets show that NCSF can achieve same recommendation quality and stronger privacy protection as the FL approach of plaintext aggregation.

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Acknowledgment

This research was financially supported by the National Natural Science Foundation of China (No. 61702318), Guangdong province special fund for science and technology (“major special projects + task list”) project (No. STKJ2021201), 2020 Li Ka Shing Foundation Cross-Disciplinary Research Grant (No. 2020LKSFG08D), Guangdong Province Basic and Applied Basic Research Fund (2021A1515012527), Science and Technology Planning Project of Guangdong Province (2019B010116001), and Special projects in key fields of Guangdong universities (No. 2022ZDZX1008).

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Authors and Affiliations

  1. College of Engineering, Shantou University, Shantou, 515063, China

    Zhuo Xu, Jian Lin, Weiwei She, Jianlong Xu, Zhi Xiong & Hao Cai

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  1. Zhuo Xu
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  2. Jian Lin
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  3. Weiwei She
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  4. Jianlong Xu
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  5. Zhi Xiong
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  6. Hao Cai
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Corresponding author

Correspondence to Jianlong Xu .

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Editors and Affiliations

  1. Louisiana State University, Baton Rouge, LA, USA

    Wang Qingyang

  2. Kingdee International Software Group Co., Ltd., Shenzhen, China

    Liang-Jie Zhang

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Xu, Z., Lin, J., She, W., Xu, J., Xiong, Z., Cai, H. (2022). Neighbor Collaboration-Based Secure Federated QoS Prediction for Smart Home Services. In: Qingyang, W., Zhang, LJ. (eds) Services Computing – SCC 2022. SCC 2022. Lecture Notes in Computer Science, vol 13738. Springer, Cham. https://doi.org/10.1007/978-3-031-23515-3_6

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  • DOI: https://doi.org/10.1007/978-3-031-23515-3_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23514-6

  • Online ISBN: 978-3-031-23515-3

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