Federated Dual-Process AI for Privacy-Preserving Distributed Decision Intelligence in Smart Cities
Keywords:
federated learning, dual-process theory, privacy preservation, smart cities, decision intelligence, distributed systems, socio-technical governance, ethical AIAbstract
The proliferation of smart city infrastructures has generated unprecedented volumes of data distributed across heterogeneous edge devices, municipal sensors, and institutional databases. Extracting actionable decision intelligence from these decentralized data sources while preserving individual privacy presents a fundamental challenge that existing centralized artificial intelligence paradigms cannot adequately address. This paper introduces a federated dual-process AI framework that integrates two complementary reasoning modalities—an intuitive, fast, pattern-based system and a deliberate, slow, analytical system—within a privacy-preserving distributed architecture. The framework synthesizes principles from cognitive science, federated learning, differential privacy, and multi-agent systems to enable scalable, robust, and fair decision-making across urban domains such as traffic management, public safety, energy distribution, and healthcare coordination. We examine structural trade-offs between reasoning speed and accuracy, local autonomy versus global coherence, and privacy guarantees versus model utility. The architecture employs secure aggregation protocols and adaptive privacy budgets to balance competing objectives while maintaining operational sustainability. Governance mechanisms are proposed to ensure algorithmic accountability and mitigate systemic biases that may arise from heterogeneous local data distributions. Deployment considerations including communication efficiency, fault tolerance, and regulatory compliance are analyzed through case illustrations from pilot smart city initiatives. The paper concludes by outlining future research directions for dual-process AI systems that can dynamically calibrate their reasoning modes in response to contextual risk, latency requirements, and societal values. This work contributes a unified conceptual framework and a set of design principles for building privacy-preserving distributed decision intelligence that is both cognitively plausible and practically deployable.
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