Federated Learning (FL) is a newly emerged decentralized machine learning
(ML) framework that combines on-device local training with server-based model
synchronization to train a centralized ML model over distributed nodes. In this
paper, we propose an asynchronous FL framework with periodic aggregation to
eliminate the straggler issue in FL systems. For the proposed model, we
investigate several device scheduling and update aggregation policies and
compare their performances when the devices have heterogeneous computation
capabilities and training data distributions. From the simulation results, we
conclude that the scheduling and aggregation design for asynchronous FL can be
rather different from the synchronous case. For example, a norm-based
significance-aware scheduling policy might not be efficient in an asynchronous
FL setting, and an appropriate "age-aware" weighting design for the model
aggregation can greatly improve the learning performance of such systems.

本文提出了一种异步 FL 框架，其采用定期聚合的方式消除了 FL 系统中的问题，并设计出多种装置调度和更新聚合策略以解决异构计算能力和训练数据分布的设备问题。通过模拟结果，我们得出结论，异步 FL 的调度和聚合设计可以不同于同步情况，并且适当的 “年龄感知” 加权设计可以极大地提高此类系统的学习性能。

异步联邦学习的设备调度和更新聚合策略

Device Scheduling and Update Aggregation Policies for Asynchronous  Federated Learning

In this paper, we propose Helios, a heterogeneity-aware FL framework to
tackle the straggler issue. Helios identifies individual devices' heterogeneous
training capability, and therefore the expected neural network model training
volumes regarding the collaborative training pace. For straggling devices, a
"soft-training" method is proposed to dynamically compress the original
identical training model into the expected volume through a rotating neuron
training approach. With extensive algorithm analysis and optimization schemes,
the stragglers can be accelerated while retaining the convergence for local
training as well as federated collaboration.

本文提出 Helios，一个针对数据异质性解决掉队者问题的联邦学习框架，采用旋转神经元训练方法将差异设备的训练量动态压缩至期望量，通过算法分析和优化方案，可以保证本地训练和联邦协作的收敛性，并加速松散设备的训练。