标题： Equitable-FL：资源受限环境下的稀疏联邦学习 显示英文标题

标题： Equitable-FL: Federated Learning with Sparsity for Resource-Constrained Environment

摘要： 在联邦学习中，模型训练是在多个计算设备上进行的，其中仅与一个公共中央服务器共享参数，而不交换其数据实例。 这种策略假设个体客户端拥有丰富的资源，并利用这些资源来构建更丰富的模型作为用户的模型。 然而，当资源丰富的假设被违反时，学习可能无法进行，因为某些节点可能无法参与该过程。 在本文中，我们提出了一种稀疏形式的联邦学习，它在资源受限环境中表现良好。 我们的目标是无论节点在空间、计算或带宽方面是否稀缺，都能实现学习。 该方法基于这样的观察：模型大小与可用资源之间的关系定义了资源稀缺性，这意味着在不影响准确性的前提下减少参数数量是资源受限环境中模型训练的关键。 在这项工作中，使用了彩票假设方法来逐步对模型进行稀疏化，以鼓励资源稀缺的节点参与协作训练。 我们在$MNIST$、$F-MNIST$和$CIFAR-10$基准数据集以及$Brain-MRI$数据和$PlantVillage$数据集上验证了 Equitable-FL。 此外，我们还研究了稀疏性对性能、模型大小压缩和训练速度提升的影响。 对训练卷积神经网络进行的实验结果验证了 Equitable-FL 在异构资源受限学习环境中的有效性。 显示英文摘要

摘要： In Federated Learning, model training is performed across multiple computing devices, where only parameters are shared with a common central server without exchanging their data instances. This strategy assumes abundance of resources on individual clients and utilizes these resources to build a richer model as user's models. However, when the assumption of the abundance of resources is violated, learning may not be possible as some nodes may not be able to participate in the process. In this paper, we propose a sparse form of federated learning that performs well in a Resource Constrained Environment. Our goal is to make learning possible, regardless of a node's space, computing, or bandwidth scarcity. The method is based on the observation that model size viz a viz available resources defines resource scarcity, which entails that reduction of the number of parameters without affecting accuracy is key to model training in a resource-constrained environment. In this work, the Lottery Ticket Hypothesis approach is utilized to progressively sparsify models to encourage nodes with resource scarcity to participate in collaborative training. We validate Equitable-FL on the $MNIST$, $F-MNIST$, and $CIFAR-10$ benchmark datasets, as well as the $Brain-MRI$ data and the $PlantVillage$ datasets. Further, we examine the effect of sparsity on performance, model size compaction, and speed-up for training. Results obtained from experiments performed for training convolutional neural networks validate the efficacy of Equitable-FL in heterogeneous resource-constrained learning environment.