A Survey on Federated Analytics: Taxonomy, Enabling Techniques, Applications and Open Issues
CoRR(2024)
摘要
The escalating influx of data generated by networked edge devices, coupled
with the growing awareness of data privacy, has promoted a transformative shift
in computing paradigms from centralized data processing to privacy-preserved
distributed data processing. Federated analytics (FA) is an emerging technique
to support collaborative data analytics among diverse data owners without
centralizing the raw data. Despite the wide applications of FA in industry and
academia, a comprehensive examination of existing research efforts in FA has
been notably absent. This survey aims to bridge this gap by first providing an
overview of FA, elucidating key concepts, and discussing its relationship with
similar concepts. We then conduct a thorough examination of FA, including its
taxonomy, key challenges, and enabling techniques. Diverse FA applications,
including statistical metrics, set computation, frequency-related applications,
database query operations, model-based applications, FL-assisting FA tasks, and
other wireless network applications are then carefully reviewed. We complete
the survey with several open research issues and future directions. This survey
intends to provide a holistic understanding of the emerging FA techniques and
foster the continued evolution of privacy-preserving distributed data
processing in the emerging networked society.
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