The Wonderful Flower of Seven Colors: Self-evolution Approach for Multi-terminal Personalized Service Processing

In mobile cloud computing, traditional frameworks often treat mobile devices as passive data collectors, relying heavily on centralized cloud centers for model processing. This centralized approach limits data-processing accuracy and efficiency due to bandwidth constraints, communication latency, and limited computational utilization of mobile devices. Furthermore, as mobile applications increasingly require real-time, personalized services, these limitations restrict model adaptability and responsiveness, emphasizing the need for a distributed approach that can dynamically adjust to varying service demands and network conditions. This paper proposes FLOM (Framework-Level Self-evolution Optimization Model), a novel framework designed to support autonomous, adaptive model optimization across multi-terminal systems. Unlike traditional methods, FLOM leverages the computational capacity of mobile devices to enable on-site model updates and intelligent data allocation through a collaborative network of cloud and mobile nodes. FLOM incorporates deep reinforcement learning for dynamic model evolution, allowing real-time adaptation across terminals. By utilizing model information from each device, FLOM achieves balanced self-evolution, optimizing processing accuracy and system resilience. The framework introduces three major innovations: (1) Self-adaptive Model Evolution by utilizing mobile terminal resources to locally update models, FLOM reduces dependence on centralized data centers, ensuring faster, context-aware adjustments that enhance real-time service delivery; (2) Intelligent Resource Allocation supporting real-time, task-targeted distribution to enhance fault tolerance and system efficiency, FLOM effectively allocates resources to handle diverse service needs across terminals; and (3) Collaborative Optimization by integrating model characteristics from multiple terminals, FLOM enables unified, system-wide optimization that improves model robustness and adaptability. Experimental results show that FLOM significantly improves data accuracy, reduces processing time, and enhances fault tolerance compared to traditional frameworks, making it a robust and versatile solution for real-time, adaptive mobile cloud applications.