Federated learning (FL) enables edge nodes to collaboratively contribute to constructing a global model without sharing their data. This is accomplished by devices computing local, private model updates that are then aggregated by a server. However, computational resource constraints and network communication can become a severe bottleneck for larger model sizes typical for deep learning applications. Edge nodes tend to have limited hardware resources (RAM, CPU), and the network bandwidth and reliability at the edge is a concern for scaling federated fleet applications. In this paper, we propose and evaluate a FL strategy inspired by transfer learning in order to reduce resource utilization on devices, as well as the load on the server and network in each global training round. For each local model update, we randomly select layers to train, freezing the remaining part of the model. In doing so, we can reduce both server load and communication costs per round by excluding all untrained layer weights from being transferred to the server. The goal of this study is to empirically explore the potential trade-off between resource utilization on devices and global model convergence under the proposed strategy. We implement the approach using the federated learning framework FEDn. A number of experiments were carried out over different datasets (CIFAR-10, CASA, and IMDB), performing different tasks using different deep-learning model architectures. Our results show that training the model partially can accelerate the training process, efficiently utilizes resources on-device, and reduce the data transmission by around 75% and 53% when we train 25%, and 50% of the model layers, respectively, without harming the resulting global model accuracy.

翻译：联邦学习（FL）使边缘节点能够在不共享数据的情况下协作构建全局模型。该过程通过各设备计算本地私有模型更新并由服务器聚合实现。然而，对于深度学习应用中常见的较大规模模型，计算资源约束与网络通信可能成为严重瓶颈。边缘节点通常具备有限的硬件资源（RAM、CPU），而边缘环境下的网络带宽与可靠性对扩展联邦集群应用构成挑战。本文提出并评估一种受迁移学习启发的联邦学习策略，以降低各全局训练轮次中设备端的资源占用，同时减轻服务器与网络负载。对于每次本地模型更新，我们随机选择部分层进行训练，并冻结其余模型层。通过排除所有未训练层参数向服务器的传输，该方法能够同时降低服务器负载与每轮通信成本。本研究旨在通过实验探索该策略下设备资源利用与全局模型收敛之间的潜在权衡。我们采用联邦学习框架FEDn实现该方法，并在不同数据集（CIFAR-10、CASA与IMDB）上开展多项实验，采用不同深度学习模型架构完成多种任务。结果表明：部分训练模型可加速训练进程、高效利用设备端资源，当训练25%和50%模型层时，数据传输量分别减少约75%和53%，且不损害最终全局模型的精度。