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      A Comprehensive Survey of Privacy-preserving Federated Learning : A Taxonomy, Review, and Future Directions

      1 , 2 , 1

      ACM Computing Surveys

      Association for Computing Machinery (ACM)

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          Abstract

          The past four years have witnessed the rapid development of federated learning (FL). However, new privacy concerns have also emerged during the aggregation of the distributed intermediate results. The emerging privacy-preserving FL (PPFL) has been heralded as a solution to generic privacy-preserving machine learning. However, the challenge of protecting data privacy while maintaining the data utility through machine learning still remains. In this article, we present a comprehensive and systematic survey on the PPFL based on our proposed 5W-scenario-based taxonomy. We analyze the privacy leakage risks in the FL from five aspects, summarize existing methods, and identify future research directions.

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          Most cited references 206

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          A Survey on Transfer Learning

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            The Algorithmic Foundations of Differential Privacy

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              Domain adaptation via transfer component analysis.

              Domain adaptation allows knowledge from a source domain to be transferred to a different but related target domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we first propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a reproducing kernel Hilbert space using maximum mean miscrepancy. In the subspace spanned by these transfer components, data properties are preserved and data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. Furthermore, in order to uncover the knowledge hidden in the relations between the data labels from the source and target domains, we extend TCA in a semisupervised learning setting, which encodes label information into transfer components learning. We call this extension semisupervised TCA. The main contribution of our work is that we propose a novel dimensionality reduction framework for reducing the distance between domains in a latent space for domain adaptation. We propose both unsupervised and semisupervised feature extraction approaches, which can dramatically reduce the distance between domain distributions by projecting data onto the learned transfer components. Finally, our approach can handle large datasets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach are verified by experiments on five toy datasets and two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.
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                Author and article information

                Journal
                ACM Computing Surveys
                ACM Comput. Surv.
                Association for Computing Machinery (ACM)
                0360-0300
                1557-7341
                July 2021
                July 2021
                : 54
                : 6
                : 1-36
                Affiliations
                [1 ]School of Engineering and Information Technology, University of New South Wales, Canberra, ACT, Australia
                [2 ]School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
                Article
                10.1145/3460427
                © 2021
                Product
                Self URI (article page): https://dl.acm.org/doi/10.1145/3460427

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