Abstract
This paper proposes a cooperative framework for resource allocation in multi-access edge computing (MEC) under a partial task offloading setting, addressing the joint challenges of learning performance and system efficiency in heterogeneous edge environments. In the proposed architecture, selected users act as edge servers (SEs) that collaboratively assist others alongside a central server (CS). A joint optimization problem is formulated to integrate model training with resource allocation while accounting for data freshness and spatial correlation among user tasks. The correlation-aware formulation penalizes outdated and redundant data, leading to improved robustness against non-i.i.d. distributions. To solve the NP-hard problem efficiently, a projected gradient descent (PGD) method is developed. The simulation results demonstrate that the proposed cooperative approach achieves a balanced delay of 0.042 s, close to edge-only computing (0.033 s) and 30% lower than the CS-only mode, while improving clustering accuracy to 99.2% (up to 15% higher than the baseline). Moreover, it reduces the central server load by nearly half, ensuring scalability and latency compliance within 3GPP limits. These findings confirm that cooperation between SEs and the CS substantially enhances reliability and performance in distributed Edge-AI system.