Spread: Decentralized Model Aggregation for Scalable Federated Learning
Chuang Hu, Huang Huang Liang, Xiao Ming Han, Bo An Liu, Dazhao Cheng, Dan Wang · 2022
Federated learning (FL) is a new distributed machine learning paradigm that enables machine learning on edge devices. One unique feature of FL is that edge devices belong to individuals; and since they are not “owned” by the FL coordinator, but can be “federated” instead, there can potentially be a huge number of edge devices. In the current distributed ML architecture, the parameter server (PS) architecture, model aggregation is centralized. When facing a large number of edge devices, the centralized model aggregation becomes the bottleneck and fundamentally restricts system scalability.