Federated learning is a privacy-preserving training method which consists of training from a plurality of clients but without sharing their confidential data. However, previous work on federated learning do not explore suitable neural network architectures for clients with different input images sizes and different numbers of output categories. In this paper, we propose an effective federated learning method named ScalableFL, where the depths and widths of the local models for each client are adjusted according to the clients' input image size and the numbers of output categories. In addition, we provide a new bound for the generalization gap of federated learning. In particular, this bound helps to explain the effectiveness of our scalable neural network approach. We demonstrate the effectiveness of ScalableFL in several heterogeneous client settings for both image classification and object detection tasks.

翻译：联邦学习是一种隐私保护的训练方法，它通过多个客户端进行联合训练，但无需共享各方的机密数据。然而，现有联邦学习研究尚未探索适用于具有不同输入图像尺寸和不同输出类别数量的客户端神经网络架构。本文提出一种名为ScalableFL的高效联邦学习方法，该方法根据客户端的输入图像尺寸和输出类别数量，动态调整各客户端本地模型的深度和宽度。此外，我们推导了联邦学习泛化差距的新上界，该上界有助于解释所提出的可扩展神经网络方法的有效性。在图像分类与目标检测任务的多种异构客户端场景中，我们验证了ScalableFL的卓越性能。