通过自适应拆分模型与资源分配，显著降低联邦学习的延迟和能耗。
ASFL: An Adaptive Model Splitting and Resource Allocation Framework for Split Federated Learning
- 根据客户端能力动态拆分模型，由服务器协助训练部分网络。
- 实验表明延迟减少75%，能耗降低80%，收敛速度更快。
- 适合计算资源有限的移动设备或物联网场景使用。
联邦学习（FL）允许多个客户端在不共享原始数据的情况下协同训练模型。然而，客户端计算资源有限可能导致训练延迟高、能耗大。本文提出一种面向无线网络的自适应分割联邦学习（ASFL）框架，利用中心服务器的计算能力训练部分模型，并实现训练过程中模型分割与资源分配的自适应调整。为优化学习性能（收敛速度）与效率（延迟与能耗），我们理论分析了收敛速率，并构建联合性能与资源分配优化问题。由于长期延迟与能耗约束及模型分割与资源分配决策的耦合性，求解极具挑战。为此，提出在线优化增强的块坐标下降（OOE-BCD）算法进行迭代求解。实验结果表明，相较于五种基线方案，所提ASFL框架收敛更快，总延迟降低最多达75%，能耗降低最多达80%。
原文摘要 · Abstract (English)
Federated learning (FL) enables multiple clients to collaboratively train a machine learning model without sharing their raw data. However, the limited computation resources of the clients may result in a high delay and energy consumption on training. In this paper, we propose an adaptive split federated learning (ASFL) framework over wireless networks. ASFL exploits the computation resources of the central server to train part of the model and enables adaptive model splitting as well as resource allocation during training. To optimize the learning performance (i.e., convergence rate) and efficiency (i.e., delay and energy consumption) of ASFL, we theoretically analyze the convergence rate and formulate a joint learning performance and resource allocation optimization problem. Solving this problem is challenging due to the long-term delay and energy consumption constraints as well as the coupling of the model splitting and resource allocation decisions. We propose an online optimization enhanced block coordinate descent (OOE-BCD) algorithm to solve the problem iteratively. Experimental results show that when compared with five baseline schemes, our proposed ASFL framework converges faster and reduces the total delay and energy consumption by up to 75% and 80%, respectively.
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