Edge AI-Based Real-Time Personalized Retrieval for Resource-Constrained Mobile Applications
Keywords:
edge AI, personalization, real-time retrieval, resource-constrained mobile systems, governance, sustainabilityAbstract
The rapid growth of mobile applications that depend on real-time personalized retrieval has exposed fundamental tensions among inference quality, latency, energy consumption, device heterogeneity, and privacy. Edge artificial intelligence offers a promising pathway for moving personalization and context reasoning closer to data generation, but its effective adoption requires careful system-level design across model compression, split inference, federated learning, caching, and governance mechanisms. This article examines edge AI for personalized retrieval in resource-constrained mobile environments. It analyzes architectural trade-offs across the device-edge-cloud continuum and discusses how local context-aware retrieval can be supported through efficient model deployment, collaborative inference, and selective synchronization. The paper further evaluates robustness, fairness, privacy, and regulatory constraints as first-class system requirements rather than peripheral concerns. Sustainability implications, including energy consumption and hardware life cycles, are also considered. Cross-domain comparisons with recommender systems, mobile edge computing, and responsible AI illustrate structural challenges and opportunities. The article concludes with forward-looking perspectives on resilient orchestration, fairness-aware personalization, and sustainable edge infrastructures. By integrating technical and governance dimensions, the paper contributes a conceptual framework for designing real-time personalized retrieval systems that are both resource-efficient and socially accountable.
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