Federated Learning for Privacy-Preserving Cloud-Native Analytics Pipelines

Abstract

This work introduces a federated learning architecture for cloud-native data analytics pipelines that preserves data privacy across distributed organizational boundaries. Experimental results across three industry datasets demonstrate comparable model accuracy to centralized training while eliminating raw data transfer.

Keywords: federated learning, cloud computing, privacy, distributed systems

Authors & Affiliations

• Ananya Rao (Corresponding Author) — Vellore Institute of Technology, India
• Lisa Wong — National University of Singapore, Singapore

How to Cite

APA: Ananya Rao, Lisa Wong (2026). Federated Learning for Privacy-Preserving Cloud-Native Analytics Pipelines. Journal of Innovative Technologies, 2(1), 1-15. https://doi.org/10.5555/jit.2026.00001

MLA: Ananya Rao, et al. "Federated Learning for Privacy-Preserving Cloud-Native Analytics Pipelines." Journal of Innovative Technologies, vol. 2, no. 1, 2026, pp. 1-15.

IEEE: Ananya Rao, Lisa Wong, "Federated Learning for Privacy-Preserving Cloud-Native Analytics Pipelines," Journal of Innovative Technologies, vol. 2, no. 1, pp. 1-15, 2026.

Chicago: Ananya Rao, Lisa Wong. "Federated Learning for Privacy-Preserving Cloud-Native Analytics Pipelines." Journal of Innovative Technologies 2, no. 1 (2026): 1-15.