Abstract
The data generated and stored in mobile devices owned by individuals as well as in various organizations contains a large amount of valuable and important information that can be used to improve service quality, user experience, and satisfaction. However, due to privacy concerns, many entities are reluctant to share their data with others, and this is a major barrier to developing comprehensive models that can provide accurate predictions. Federated learning is a state-of-the-art distributed machine learning approach where multiple clients are allowed to collaboratively train a model while keeping their private training data locally. Although federated learning seems to be a viable solution for jointly training a machine learning model without compromising privacy, sensitive privacy information may still be leaked through shared model parameters and query results. Over the past six years, the researchers have extensively studied privacy protection enhancements of federated learning, and they have revealed that general privacy protection mechanisms can be adopted to mitigate privacy issues of federated learning. However, protecting privacy through federated learning while maintaining data utility is still an open issue. This article provides an overview of federated learning while discussing privacy leakages, possible defense mechanisms, and future research directions of privacy-preserved federated learning.
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Literature search and analysis: Hashan Ratnayake, Lin Chen; Writing - original draft preparation: Hashan Ratnayake; Writing - review and editing: Xiaofeng Ding, Lin Chen; Supervision: Xiaofeng Ding.
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Ratnayake, H., Chen, L. & Ding, X. A review of federated learning: taxonomy, privacy and future directions. J Intell Inf Syst 61, 923–949 (2023). https://doi.org/10.1007/s10844-023-00797-x
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DOI: https://doi.org/10.1007/s10844-023-00797-x