Abstract
Privacy is an essential human-centric challenges to address for the success of the SEC paradigm. This is especially true for health-related applications in SEC. In this chapter, we review the work that studies the above challenge in a particular category of smart health application for SEC—Abnormal Health Detection Systems (AHDS). In particular, we present FedSens, a new federated learning framework dedicated to addressing the imbalanced data problem in AHDS applications with explicit considerations of participant privacy and device resource constraints.
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Wang, D., Zhang, D.‘. (2023). Privacy in Social Edge. In: Social Edge Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-26936-3_7
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DOI: https://doi.org/10.1007/978-3-031-26936-3_7
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