Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems

Saidala ; Ravi Kumar ; Iftikhar ; Umna ; Hasanov ; Tofig ; Mammadli ; Vüqar Ahmad
TechComp Innovations: Journal of Computer Science... 📅 2025 Vol 2, No 2 🔗 DOI: 10.70063/techcompinnovations.v2i2.124
Abstract
This study examines the emerging paradigm of federated intelligence architectures as a secure, privacy-preserving, and scalable foundation for data-driven innovation across AI, IoT, and cloud ecosystems. With billions of interconnected devices generating massive heterogeneous data, traditional centralized machine-learning models face critical limitations, including privacy risks, regulatory constraints, latency, and single points of failure. Through a qualitative content-analysis approach, this paper synthesizes contemporary research on federated learning, blockchain integration, zero-trust governance, and edge intelligence to formulate a comprehensive understanding of distributed AI infrastructures. The findings highlight that federated learning enables collaborative model training without exposing raw data, significantly enhancing privacy, security, and compliance. Moreover, combining blockchain with federated learning strengthens auditability, model integrity, and trust, while zero-trust principles provide continuous verification and adaptive security enforcement across devices. Edge-AI integration further reduces latency and bandwidth consumption, enabling real-time analytics in resource-constrained IoT environments. Collectively, these elements contribute to the formation of cognitive ecosystems capable of autonomous, interoperable, and context-aware operations. The study underscores the transformative potential of federated intelligence while identifying critical gaps that inform future research trajectories.
Keywords
Federated Intelligence; IoT Ecosystems; Blockchain-enabled Security; Zero-Trust Architecture; Edge AI
How to Cite

Saidala, et al. (2025). Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems. TechComp Innovations: Journal of Computer Science and Technology, 2(2). https://doi.org/10.70063/techcompinnovations.v2i2.124

Saidala, Ravi Kumar; Iftikhar, Umna; Hasanov, Tofig; Mammadli, Vüqar Ahmad, "Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems," TechComp Innovations: Journal of Computer Science and Technology, vol. 2, no. 2, 2025.

Saidala, Ravi Kumar; Iftikhar, Umna; Hasanov, Tofig; Mammadli, Vüqar Ahmad. "Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems." TechComp Innovations: Journal of Computer Science and Technology, vol. 2, no. 2, 2025.

Saidala, Ravi Kumar; Iftikhar, Umna; Hasanov, Tofig; Mammadli, Vüqar Ahmad. "Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems." TechComp Innovations: Journal of Computer Science and Technology 2, no. 2 (2025).

Saidala, et al. (2025) 'Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems', TechComp Innovations: Journal of Computer Science and Technology, 2(2). doi: 10.70063/techcompinnovations.v2i2.124.

Saidala, Ravi Kumar; Iftikhar, Umna; Hasanov, Tofig; Mammadli, Vüqar Ahmad. Federated Intelligence Architectures for Secure, Data-Driven Innovation Across AI, IoT, and Cloud Ecosystems. TechComp Innovations: Journal of Computer Science and Technology. 2025;2(2).

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