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Privacy-Enhanced Federated Learning with Weighted Aggregation

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Security and Privacy in Social Networks and Big Data (SocialSec 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1495))


The pervasive adoption of machine learning (ML) techniques by social network operators has led to a growing concern in the personal data privacy of their customers. ML inevitably accesses and processes users’ personal data, which could potentially breach the relevant privacy protection regulations if not performed carefully. In this backdrop, Federated Learning (FL) is an emerging area that allows ML on distributed data without the data leaving their stored location. Typically, FL starts with an initial global model, with each datastore uses its local data to compute the gradient based on the global model, and uploads their gradients (instead of the data) to an aggregation server, at which the global model is updated and then distributed to the local datastores iteratively. However, depending on the nature of the services operated by social networks, data captured at different locations may carry different significance to the business operation, hence a weighted aggregation will be highly desirable for enhancing the quality of the FL model. Furthermore, to prevent the data leakage from aggregated gradients, cryptographic mechanisms are needed to allow secure aggregation of FL. As such, this paper proposes a privacy-enhanced FL scheme, based on cryptographic mechanisms that allow both the data significance evaluation and weighted aggregation of local models in a privacy-preserving manner. Experimental results show that our scheme is practical and secure.

J. Guo and Z. Liu—Both authors contributed equally to this research.

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  1. 1.

    We use the terms secure and privacy-preserving interchangeably.

  2. 2.

    We use the terms user and client interchangeably.


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Correspondence to Jiale Guo or Ziyao Liu .

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Guo, J., Liu, Z., Lam, KY., Zhao, J., Chen, Y. (2021). Privacy-Enhanced Federated Learning with Weighted Aggregation. In: Lin, L., Liu, Y., Lee, CW. (eds) Security and Privacy in Social Networks and Big Data. SocialSec 2021. Communications in Computer and Information Science, vol 1495. Springer, Singapore.

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  • Print ISBN: 978-981-16-7912-4

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