The increasing complexity of deep neural networks poses significant barriers
to democratizing them to resource-limited edge devices. To address this
challenge, split federated learning (SFL) has emerged as a promising solution
by of floading the primary training workload to a server via model partitioning
while enabling parallel training among edge devices. However, although system
optimization substantially influences the performance of SFL under
resource-constrained systems, the problem remains largely uncharted. In this
paper, we provide a convergence analysis of SFL which quantifies the impact of
model splitting (MS) and client-side model aggregation (MA) on the learning
performance, serving as a theoretical foundation. Then, we propose AdaptSFL, a
novel resource-adaptive SFL framework, to expedite SFL under
resource-constrained edge computing systems. Specifically, AdaptSFL adaptively
controls client-side MA and MS to balance communication-computing latency and
training convergence. Extensive simulations across various datasets validate
that our proposed AdaptSFL framework takes considerably less time to achieve a
target accuracy than benchmarks, demonstrating the effectiveness of the
proposed strategies.

通过模型分割和客户端模型聚合，我们提出了一种适应资源受限边缘计算系统的新型资源自适应分割联邦学习（AdaptSFL）框架，以加速分割联邦学习并实现更好的训练性能。

适应性资源受限边缘网络中的自适应拆分联邦学习

AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge  Networks

Device-edge co-inference opens up new possibilities for resource-constrained
wireless devices (WDs) to execute deep neural network (DNN)-based applications
with heavy computation workloads. In particular, the WD executes the first few
layers of the DNN and sends the intermediate features to the edge server that
processes the remaining layers of the DNN. By adapting the model splitting
decision, there exists a tradeoff between local computation cost and
communication overhead. In practice, the DNN model is re-trained and updated
periodically at the edge server. Once the DNN parameters are regenerated, part
of the updated model must be placed at the WD to facilitate on-device
inference. In this paper, we study the joint optimization of the model
placement and online model splitting decisions to minimize the energy-and-time
cost of device-edge co-inference in presence of wireless channel fading. The
problem is challenging because the model placement and model splitting
decisions are strongly coupled, while involving two different time scales. We
first tackle online model splitting by formulating an optimal stopping problem,
where the finite horizon of the problem is determined by the model placement
decision. In addition to deriving the optimal model splitting rule based on
backward induction, we further investigate a simple one-stage look-ahead rule,
for which we are able to obtain analytical expressions of the model splitting
decision. The analysis is useful for us to efficiently optimize the model
placement decision in a larger time scale. In particular, we obtain a
closed-form model placement solution for the fully-connected multilayer
perceptron with equal neurons. Simulation results validate the superior
performance of the joint optimal model placement and splitting with various DNN
structures.

研究了设备 - 边缘共同推理的联合优化问题，其中包括在线模型分裂和模型放置决策，以最小化存在无线信道衰落时设备 - 边缘共同推理的能量和时间成本。