这个新框架把联邦学习和分割学习结合起来，用量化省电，还能控制精度，比传统方法更适合手机这类设备跑大模型。
GQ-FSL在联邦分割学习（FSL）中引入随机量化，对客户端和服务器的子模型使用非对称精度，从而解耦设备能耗与全局收敛损失。该方法建立了参数化能耗模型，并推导了数据异构下的理论收敛界。通过联合优化DNN分割点和精度级别，GQ-FSL在满足准确率约束的前提下最小化总系统能耗。实验显示，相比量化联邦学习和全精度FSL，GQ-FSL显著提升能效，支持在资源受限设备上部署大型DNN。
GQ-FSL: Green Quantized Federated Split Learning
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. While federated split learning (FSL) mitigates on-device computation by offloading workloads to an edge server, this may introduce systemic overheads, while the continuous exchange of cut-layer data, 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 a strict target accuracy constraint. 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.