Collaborative Edge–Cloud Multimodal Foundation Models for Low-Latency Visual Generation in 6G Intelligent Networks
Keywords:
6G networks, edge–cloud collaboration, multimodal foundation models, visual generation, low-latency AI, model partitioning, sustainability, governanceAbstract
The convergence of sixth-generation (6G) networks and large-scale artificial intelligence heralds a new era of immersive and context-aware visual services, demanding low-latency generation of high-fidelity imagery directly at the network edge. This paper presents a comprehensive system-level investigation of collaborative edge–cloud multimodal foundation models designed for low-latency visual generation within 6G intelligent networks. We analyze the architectural integration of distributed foundation models that jointly exploit edge-based inference acceleration and cloud-scale semantic reasoning to meet stringent latency requirements while preserving output quality. The study examines the structural trade-offs between model partitioning, communication overhead, and computational heterogeneity across edge–cloud continuums, emphasizing the role of multimodal conditioning mechanisms that fuse textual, spatial, and environmental sensor data. Beyond performance, the paper critically evaluates governance frameworks, infrastructure sustainability, robustness under dynamic network conditions, fairness in service delivery, and policy implications for large-scale deployment. Through a discursive analysis of ongoing standardization efforts, federated learning paradigms, and emerging edge-native training techniques, we articulate a roadmap for resilient and ethically grounded visual generation systems. The discussion highlights the necessity of cross-layer design principles that align physical resource orchestration with AI workload characteristics, ultimately advocating for a socio-technical perspective that incorporates energy proportionality, equitable access, and regulatory compliance into the technical fabric of 6G-enabled edge intelligence.
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