CoCo-Fed：无线边缘内存和通信高效联邦学习的统一框架
CoCo-Fed: A Unified Framework for Memory- and Communication-Efficient Federated Learning at the Wireless Edge
AI总结：
研究针对O-RAN架构中大规模神经网络部署的瓶颈，提出CoCo-Fed框架，通过本地梯度双维下投影及全局正交子空间叠加传输协议，统一内存与通信效率，理论证明其收敛性，模拟显示在相关任务中优于基线。
摘要：
在开放无线接入网络（O-RAN）架构中部署大规模神经网络对于实现原生边缘智能至关重要。然而，该范式面临两个关键瓶颈：资源受限的gNB上进行本地训练所需的内存占用过高，以及在高维模型更新的全局聚合期间带宽受限的回程链路饱和。为应对这些挑战，我们提出了CoCo-Fed，一种基于压缩和组合的新型联邦学习框架，它统一了本地内存效率和全局通信减少。在本地，CoCo-Fed通过对梯度进行双维下投影来突破内存墙，使优化器在低秩结构上运行而不引入额外的推理参数/延迟。在全局上，我们引入了一种基于正交子空间叠加的传输协议，其中每层更新被投影并叠加到每个gNB的单个合并矩阵中，大幅减少回程流量。除了经验设计，我们还建立了严格的理论基础，证明了CoCo-Fed即使在适用于无线传感任务的无监督学习条件下也能收敛。在到达角估计任务上的大量模拟表明，CoCo-Fed在内存和通信效率方面显著优于现有基线，同时在非IID设置下保持稳健收敛。
英文摘要：
The deployment of large-scale neural networks within the Open Radio Access Network (O-RAN) architecture is pivotal for enabling native edge intelligence. However, this paradigm faces two critical bottlenecks: the prohibitive memory footprint required for local training on resource-constrained gNBs, and the saturation of bandwidth-limited backhaul links during the global aggregation of high-dimensional model updates. To address these challenges, we propose CoCo-Fed, a novel Compression and Combination-based Federated learning framework that unifies local memory efficiency and global communication reduction. Locally, CoCo-Fed breaks the memory wall by performing a double-dimension down-projection of gradients, adapting the optimizer to operate on low-rank structures without introducing additional inference parameters/latency. Globally, we introduce a transmission protocol based on orthogonal subspace superposition, where layer-wise updates are projected and superimposed into a single consolidated matrix per gNB, drastically reducing the backhaul traffic. Beyond empirical designs, we establish a rigorous theoretical foundation, proving the convergence of CoCo-Fed even under unsupervised learning conditions suitable for wireless sensing tasks. Extensive simulations on an angle-of-arrival estimation task demonstrate that CoCo-Fed significantly outperforms state-of-the-art baselines in both memory and communication efficiency while maintaining robust convergence under non-IID settings.
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