Federated Learning (FL) is a distributed learning paradigm that can learn a global or personalized model from decentralized datasets on edge devices. However, in the computer vision domain, model performance in FL is far behind centralized training due to the lack of exploration in diverse tasks with a unified FL framework. FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection and image segmentation. To bridge the gap and facilitate the development of FL for computer vision tasks, in this work, we propose a federated learning library and benchmarking framework, named FedCV, to evaluate FL on the three most representative computer vision tasks: image classification, image segmentation, and object detection. We provide non-I.I.D. benchmarking datasets, models, and various reference FL algorithms. Our benchmark study suggests that there are multiple challenges that deserve future exploration: centralized training tricks may not be directly applied to FL; the non-I.I.D. dataset actually downgrades the model accuracy to some degree in different tasks; improving the system efficiency of federated training is challenging given the huge number of parameters and the per-client memory cost. We believe that such a library and benchmark, along with comparable evaluation settings, is necessary to make meaningful progress in FL on computer vision tasks. FedCV is publicly available: https://github.com/FedML-AI/FedCV.

翻译：联邦学习联合会(FL)是一个分布式学习模式,可以从边缘设备上分散的数据集中学习一个全球或个性化模型,但是,在计算机视野领域,FL的模型性能远远落后于集中培训,因为缺乏对统一FL框架的各种任务进行探索。FL很少在先进的计算机远景任务(如物体探测和图像分割)中得到有效展示。为了缩小差距并促进FL的计算机远景任务开发,在这项工作中,我们提议一个名为FCCV的联邦化学习图书馆和基准框架,以评价FL三项最具有代表性的计算机远景任务:图像分类、图像分割和目标探测。我们提供非I.D.基准数据集、模型和各种参考FL算法等。我们的基准研究表明,有许多挑战值得将来探索:中央化培训技巧可能不直接应用于FL;非I.I.D.数据集实际上将模型精确度降为不同任务的某种程度;由于参数数量庞大,FC的配置培训的系统效率具有挑战性,而且每部客户间记忆基准中,我们相信FC的这种评估是可比较性的。