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Towards Efficient and Privacy-Preserving Service QoS Prediction with Federated Learning

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2020)

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Abstract

Cloud computing provides many service resources that enable large-scale cloud applications composed of services to be widely adopted in many crucial domains. Quality of Service (QoS) is often used as an indicator in service selection and composition to guarantee the quality of cloud applications. To facilitate QoS-based selection and composition, previous studies have employed collaborative filtering techniques to predict unknown QoS values as a supplement to limited user-perceived QoS data. However, Collaborative modeling approaches encounter privacy issues in the practice of QoS prediction. Users may be reluctant to collaborate through sharing data. As a result, addressing privacy threats has become a key effort towards making QoS prediction methods practical. In this paper, we leverage federated learning techniques and propose a privacy-preserving QoS prediction approach to address this challenge. We further propose several efficiency improvement techniques to significantly reduce system overhead so that the prediction model can provide results quickly and timely. We conduct experiments on a large-scale real-world QoS dataset to evaluate our approach, and the experimental results show that it can make fast and accurate predictions.

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Acknowledgment

The work described in this paper was supported by the National Natural Science Foundation of China (61802003), and the Anhui Innovation Program for Overseas Students.

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Authors and Affiliations

  1. Anhui Normal University, Wuhu, China

    Yilei Zhang, Xiao Zhang & Xinyuan Li

  2. The Chinese University of Hong Kong, Hong Kong, China

    Yilei Zhang

Authors
  1. Yilei Zhang
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  2. Xiao Zhang
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  3. Xinyuan Li
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Corresponding author

Correspondence to Yilei Zhang .

Editor information

Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

  3. London South Bank University, London, UK

    Muddesar Iqbal

  4. Hangzhou Dianzi University, Hangzhou, China

    Yuyu Yin

  5. Zhejiang University, Hangzhou, China

    Jianwei Yin

  6. Fudan University, Shanghai, China

    Ning Gu

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Zhang, Y., Zhang, X., Li, X. (2021). Towards Efficient and Privacy-Preserving Service QoS Prediction with Federated Learning. In: Gao, H., Wang, X., Iqbal, M., Yin, Y., Yin, J., Gu, N. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 350. Springer, Cham. https://doi.org/10.1007/978-3-030-67540-0_3

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  • DOI: https://doi.org/10.1007/978-3-030-67540-0_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-67539-4

  • Online ISBN: 978-3-030-67540-0

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