GLow让去中心化学习模拟更简单，支持自定义网络拓扑测试。
GLow -- A Novel, Flower-Based Simulated Gossip Learning Strategy
- 基于Flower框架构建去中心化通信模拟系统
- 在MNIST/CIFAR10上达到98%和75%准确率
- 适合研究去中心化学习的算法设计者
完全去中心化的学习算法仍处于发展初期。由于收敛难题和去中心化系统固有的拜占庭故障，构建模块化的八卦学习策略颇具挑战。本文提出GLow，通过利用先进的Flower框架，为研究人员提供一种模拟定制化八卦学习系统的新方法。该方法可在实际部署前，评估设备在不同网络拓扑下的可扩展性与收敛性。尽管Flower框架原生仅支持集中式联邦学习策略，缺乏去中心化能力，但GLow填补了这一空白。在MNIST和CIFAR10数据集上的实验结果表明，其准确率分别超过0.98和0.75。更重要的是，所有实验中GLow在准确率和收敛性能上均与对应的集中式和联邦学习方法表现相当。
原文摘要 · Abstract (English)
Fully decentralized learning algorithms are still in an early stage of development. Creating modular Gossip Learning strategies is not trivial due to convergence challenges and Byzantine faults intrinsic in systems of decentralized nature. Our contribution provides a novel means to simulate custom Gossip Learning systems by leveraging the state-of-the-art Flower Framework. Specifically, we introduce GLow, which will allow researchers to train and assess scalability and convergence of devices, across custom network topologies, before making a physical deployment. The Flower Framework is selected for being a simulation featured library with a very active community on Federated Learning research. However, Flower exclusively includes vanilla Federated Learning strategies and, thus, is not originally designed to perform simulations without a centralized authority. GLow is presented to fill this gap and make simulation of Gossip Learning systems possible. Results achieved by GLow in the MNIST and CIFAR10 datasets, show accuracies over 0.98 and 0.75 respectively. More importantly, GLow performs similarly in terms of accuracy and convergence to its analogous Centralized and Federated approaches in all designed experiments.
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