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.

我们提出了一种高效的分布式联合学习算法（ESFL），通过在服务器和终端设备之间将模型分割为不同的子模型，综合考虑用户异构性，同时优化用户端工作量和服务器端计算资源分配，以充分利用中央服务器的强大计算能力。与标准的联合学习、分割学习和分割联合学习相比，通过大量模拟实验证明我们的 ESFL 方法显著提高了效率。