发表机构
Tecip Institute, Scuola Superiore Sant’Anna; Saint Louis University（圣安娜高等学校Tecip研究所; 圣路易斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文针对移动RAN中联邦大语言模型微调产生的异步通信负载问题，提出将RAN作为学习感知流量整形器，通过gNB网络内聚合和光连接动态配置，实现高效传输资源调度。
AI 中文摘要
联邦大语言模型微调能够利用网络边缘的私有和地理分布数据来适配大型模型，从而在接入网和传输网中产生周期性且对截止时间敏感的通信负载。这一挑战在移动RAN中尤为突出，因为无线变异性、移动性和设备异构性会导致模型更新异步到达。尽管这些更新属于同一学习轮次，共享共同的目的地和截止时间，但传统传输网络将它们视为独立的设备发起流，隐藏了其底层结构，限制了高效配置传输资源的能力。这种不匹配对于光电路交换和全光子传输尤为棘手，因为这些技术受益于可预测和可调度的流量需求。我们认为，未来的RAN应作为学习感知的流量整形器，将分布式模型适配的通信结构暴露给传输层。通过在gNB处进行网络内聚合，异步UE更新可以转化为数量更少、大小和交付要求有界的聚合传输。经过这种整形后，联邦大语言模型流量成为选择性配置光连接的合适候选者，其中高容量路径可以在聚合传输窗口期间建立，并在学习轮次之间释放。由此产生的架构结合了基于分组的移动接入的灵活性与动态配置的光容量，展示了一种在可编程接入和传输网络中协调分布式AI工作负载的更广泛方法。
英文摘要
Federated LLM fine-tuning enables large models to be adapted using private and geographically distributed data at the network edge, creating recurring and deadline-sensitive communication workloads across access and transport networks. This challenge is particularly relevant in mobile RANs, where wireless variability, mobility, and device heterogeneity cause model updates to arrive asynchronously. Although these updates belong to the same learning round and share a common destination and deadline, conventional transport networks treat them as independent device-originated flows, hiding their underlying structure and limiting the ability to efficiently provision transport resources. This mismatch is particularly problematic for optical circuit switching and all-photonics transport, which benefit from predictable and schedulable traffic demands. We argue that future RANs should act as learning-aware traffic shapers by exposing the communication structure of distributed model adaptation to the transport layer. Through in-network aggregation at the gNB, asynchronous UE updates can be transformed into fewer aggregate transfers with bounded size and delivery requirements. Once shaped in this way, federated LLM traffic becomes a suitable candidate for selectively provisioned optical connectivity, where high-capacity paths can be established during aggregate-transfer windows and released between learning rounds. The resulting architecture combines the flexibility of packet-based mobile access with dynamically provisioned optical capacity, illustrating a broader approach for coordinating distributed AI workloads across programmable access and transport networks.
