A recent emphasis of distributed learning research has been on federated learning (FL), in which model training is conducted by the data-collecting devices. Existing research on FL has mostly focused on a star topology learning architecture with synchronized (time-triggered) model training rounds, where the local models of the devices are periodically aggregated by a centralized coordinating node. However, in many settings, such a coordinating node may not exist, motivating efforts to fully decentralize FL. In this work, we propose a novel methodology for distributed model aggregations via asynchronous, event-triggered consensus iterations over the network graph topology. We consider heterogeneous communication event thresholds at each device that weigh the change in local model parameters against the available local resources in deciding the benefit of aggregations at each iteration. Through theoretical analysis, we demonstrate that our methodology achieves asymptotic convergence to the globally optimal learning model under standard assumptions in distributed learning and graph consensus literature, and without restrictive connectivity requirements on the underlying topology. Subsequent numerical results demonstrate that our methodology obtains substantial improvements in communication requirements compared with FL baselines.

本文介绍了一种用于分布式模型聚合的新方法，旨在处理分散式 Federated Learning 中的去中心化问题。方法采用异步、事件触发的共识迭代实现模型的分布式聚合，并通过异构通讯事件阈值权衡本地模型参数的变化，达到每次迭代的聚合收益最大化。理论上分析表明，该方法在分布式学习和图形共识文献中的标准假设下实现了全局最优学习模型的渐近收敛，而不需要对底层拓扑连接性有任何限制。相关数值结果表明，该方法相对于FL基线获得了大幅的通信要求改进。

异构通信阈值下的去中心化事件触发联邦学习