Federated learning (FL) allows multiple parties (distributed devices) to train a machine learning model without sharing raw data. How to effectively and efficiently utilize the resources on devices and the central server is a highly interesting yet challenging problem. In this paper, we propose an efficient split federated learning algorithm (ESFL) to take full advantage of the powerful computing capabilities at a central server under a split federated learning framework with heterogeneous end devices (EDs). By splitting the model into different submodels between the server and EDs, our approach jointly optimizes user-side workload and server-side computing resource allocation by considering users' heterogeneity. We formulate the whole optimization problem as a mixed-integer non-linear program, which is an NP-hard problem, and develop an iterative approach to obtain an approximate solution efficiently. Extensive simulations have been conducted to validate the significantly increased efficiency of our ESFL approach compared with standard federated learning, split learning, and splitfed learning.

翻译：联邦学习（FL）允许多方（分布式设备）在不共享原始数据的情况下训练机器学习模型。如何有效且高效地利用设备和中心服务器的资源是一个极具吸引力但具有挑战性的问题。本文提出了一种高效的分割联邦学习算法（ESFL），以在异构终端设备（EDs）的分割联邦学习框架下充分利用中心服务器的强大计算能力。通过将模型在服务器与EDs之间分割为不同子模型，我们的方法联合优化了用户端工作负载和服务器端计算资源分配，充分考虑了用户的异构性。我们将整个优化问题建模为一个混合整数非线性规划（NP难问题），并开发了一种迭代方法以高效获得近似解。大量仿真实验验证了我们的ESFL方法相比标准联邦学习、分割学习和分割联邦学习在效率上的显著提升。