Cache-aware Data Sensing Allocation for Personalized Edge Intelligence

In personalized edge learning (PEL), data is essentially a form of labor. Nevertheless, existing resource-scarce edge devices tend to cache the data they sense in isolation. This creates data silos that significantly degrade the quality of edge learning models. To address this impasse, we propose a cache-aware collaborative federated system, where data sensing allocation is optimized based on both local learning requirements and scenario-specific sharing feedback. Specifically, we establish a unified system reward model that explicitly decomposes the overall utility into (i) accuracy gains from local PEL and (ii) data-sharing rewards under two coexisting data sharing paradigms. For the alliance paradigm, we derive an optimal allocation policy by analyzing the properties of sensing allocation rewards. For the reward-feedback paradigm, we further design an asymptotically optimal allocation strategy using the exact penalty method. Extensive simulations demonstrate that the proposed algorithms outperform state-of-the-art approaches.