Journal of Advances in Developmental Research

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Aegis: An AI-Driven Governance Framework for Micro-Frontend Architectures using Trust-Based Federated Learning

Author(s) Mohan Siva Krishna Konakanchi
Country United States
Abstract Micro-frontend (MFE) architectures promise team autonomy and independent deployments for large web platforms, but they also introduce governance fragmentation across orga-nizational and technical silos. This fragmentation complicates integrity assurance, accountability, and consistent risk controls when numerous independently released frontend artifacts in-teract with shared identity, APIs, and customer journeys. In parallel, organizations increasingly seek AI-driven governance that learns from operational telemetry (e.g., client-side errors, security signals, deployment provenance, and runtime policy outcomes) without centralizing sensitive data across teams or business units.
This paper proposes Aegis, an AI-driven governance frame-work for MFEs that uses a trust metric-based federated learning (FL) approach to coordinate integrity and accountability across silos. Aegis introduces (i) a Trust Metric that scores each MFE contributor (team, module, or tenant) based on evidence such as update consistency, policy adherence, operational reliability, and attested provenance; (ii) a Trust-Aware Federated Aggregation mechanism that down-weights suspicious or low-accountability participants while preserving privacy and autonomy; and (iii) an Explainability–Performance Trade-off Controller that quanti-fies how governance decisions balance predictive performance (e.g., risk detection accuracy) against explanation quality (e.g., fidelity and actionability), enabling organizations to optimize for regulatory readiness and engineering usability.
We describe the Aegis architecture, a practical methodology for trust-based FL governance in MFEs, and an experimental evaluation using a prototype simulation of multi-team MFE telemetry with adversarial and non-IID behaviors. Results indi-cate that trust-aware aggregation improves robustness to faulty or compromised participants, and that explicit optimization of explanation budgets can preserve meaningful interpretability with limited performance degradation. The paper concludes with engineering guidance, limitations, and future directions for production deployment.
Keywords micro-frontends, governance, federated learn-ing, trust metrics, accountability, integrity, explainable AI, soft-ware architecture.
Field Engineering
Published In Volume 17, Issue 1, January-June 2026
Published On 2026-02-15
Cite This Aegis: An AI-Driven Governance Framework for Micro-Frontend Architectures using Trust-Based Federated Learning - Mohan Siva Krishna Konakanchi - IJAIDR Volume 17, Issue 1, January-June 2026. DOI 10.71097/IJAIDR.v17.i1.1699
DOI https://doi.org/10.71097/IJAIDR.v17.i1.1699
Short DOI https://doi.org/hbphzp
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