Federated Analytics: Insights Without Centralized Data
Author(s): Dheeraj Vaddepally
Publication #: 2601023
Date of Publication: 28.01.2026
Country: United States
Pages: 1-10
Published In: Volume 12 Issue 1 January-2026
DOI: https://doi.org/10.62970/IJIRCT.v12.i1.2601023
Abstract
Federated analytics offers a paradigm shift in analysis. This is due to the availability of insights of distributional data minus the burden of centralized data storage. Generally, this paper presents principles and methodological approach as used in federated analytics of statistical analysis of patterns detection within federated statistical analytics, an application that covers various strict demands of data secrecy. Federated analytics directly tackles critical concerns that include privacy and risks in the centralized management of data through federated learning, secure multi-party computation, and differential privacy. This work dives into practical applications and emerging opportunities within these domains, showing how federated analytics can provide powerful, privacy-preserving insights in compliance with the legal and ethical standards. The paper talks about federated analytics’ present-day limits and future as of now focusing both on scalability and security aspects where the scope is needed with further collaboration towards decentralized data analysis.
Keywords: Federated analytics, statistical analysis, pattern detection, federated learning, privacy, data secrecy, secure multi-party computation, differential privacy, decentralized data analysis, scalability.
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