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.

翻译：在联邦学习中，模型训练在多个计算设备上执行，仅将参数与公共中央服务器共享，而无需交换各自的数据实例。该策略假设各个客户端拥有充足资源，并利用这些资源构建更丰富的用户模型。然而，当资源充裕的假设不成立时，部分节点可能无法参与学习过程，导致学习无法进行。本文提出了一种稀疏形式的联邦学习，在资源受限环境下表现出色。我们的目标是使学习成为可能，无论节点在存储空间、计算能力或带宽方面是否存在稀缺性。该方法基于以下观察：模型规模相对于可用资源定义了资源稀缺性，因此在不影响精度的前提下减少参数数量是资源受限环境下模型训练的关键。本研究利用彩票假设方法逐步稀疏化模型，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$Brain-MRI$和$PlantVillage$数据集上验证了Equitable-FL的有效性。此外，我们研究了稀疏性对性能、模型体积压缩和训练加速的影响。卷积神经网络训练实验的结果证实了Equitable-FL在异构资源受限学习环境中的有效性。