AI 中文总结
针对无线边缘部署DNN的能量瓶颈，提出GQ-FSL框架，通过非对称精度的随机量化优化，实现资源受限设备上DNN的高效部署，能量效率优于量化联邦学习及全精度FSL。
AI 中文摘要
在无线边缘部署最先进的深度神经网络（DNN），会受到移动设备严格的能量与资源约束的严重瓶颈。联邦拆分学习（FSL）虽通过将工作负载卸载至边缘服务器减轻了设备端计算负担，但会引入系统性开销，且切分层数据与子模型的持续交换仍会产生显著的能量消耗（EC）。为解决该问题，我们提出绿色量化联邦拆分学习（GQ-FSL）框架，该框架将随机量化应用于本地协同训练与无线传输。值得注意的是，GQ-FSL支持客户端与服务器端子模型的非对称精度级别，有效将设备能量约束与全局收敛退化解耦。为量化这些权衡，我们为拆分架构开发参数化能量模型，并在统计异质数据下推导理论收敛界。基于此，我们构建联合优化问题以配置DNN拆分点与精度级别，在满足严格目标精度约束的同时最小化系统总EC。最终，我们证明GQ-FSL可在资源受限设备上实现大规模DNN部署，与量化联邦学习及全精度FSL相比，其能量效率更优。
英文摘要
Deploying state-of-the-art deep neural networks (DNNs) at the wireless edge is severely bottlenecked by the strict energy and resource constraints of mobile devices. Although federated split learning (FSL) alleviates on-device computational burdens by offloading workloads to an edge server, this may introduce systemic overheads, while the continuous exchange of intermediate activations, gradients, and submodels still incurs significant energy consumption (EC). To address this, we propose a green quantized FSL (GQ-FSL) framework that incorporates stochastic quantization for both local collaborative training and wireless transmissions. Notably, GQ-FSL supports asymmetric precision levels for the client- and server-side submodels, effectively decoupling device energy constraints from global convergence degradation. To quantify these tradeoffs, we develop parameterized energy models for the split architecture and derive a theoretical convergence bound under statistically heterogeneous data. Building on that, we formulate a joint optimization problem to configure the DNN split point and precision levels, minimizing the total system EC while satisfying strict latency and target accuracy constraints. Ultimately, we demonstrate that GQ-FSL enables large-scale DNN deployment on resource-constrained devices, achieving superior energy efficiency compared to quantized federated learning and full-precision FSL.
CommentsSubmitted to IEEE Transactions on Mobile Computing (TMC). This is an extended version of the work accepted to IEEE SPAWC 2026
