Toward Sustainable Big Data Analytics: A Review of Privacy-preserving Federated Learning
Keywords:
Data privacy protection, Differential privacy techniques, Distributed and decentralized analytics, Federated learning, Privacy-aware big data analytics, Secure data sharing frameworks, Secure model aggregationAbstract
The accelerated growth of data-driven systems in health services, the banking sector, automated transport networks, infrastructures, and the internet of things has increased issues linked to data privacy, secure data exchange, growth capability and extended durability of data analysis systems. Traditional centrally controlled massive data analytical processing, which collects unprocessed information at a core central system, encounters privacy disclosure threats, governance-based limitations, elevated data transmission operational load, and heavy power utilization, causing these systems to become gradually inefficient in massive and diverse settings. Federated learning (FL) has developed as a sustainable and privacy-preserving framework by supporting non-centralized predictive model learning while maintaining data at on-site endpoints, thereby decreasing data transfer and enabling legal adherence. This study provides a brief overview of privacy-preserving big data analytics utilizing federated learning with a focus on data privacy protection approaches decentralized analysis, and secure data sharing frameworks. Crucial methods, including differential privacy, secure aggregation, homomorphic encryption, and secure multi-party computation, are evaluated to measure their efficiency in minimizing data exposure from learning model modifications. The overview emphasizes essential exchanges between confidentiality effectiveness, data transmission optimization and scalability and examines federated learning with conventional single-server analysis from a sustainability viewpoint. Lastly, essential investigation shortcomings are determined: restricted applied implementations, insufficient management of non-IID data, absence of consistent assessment metrics and poor interoperability with current big data frameworks. This research intends to facilitate the stated design of secure, scalable, and sustainable big data analytics architectures.
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