In this work, we examine a network of agents operating asynchronously, aiming
to discover an ideal global model that suits individual local datasets. Our
assumption is that each agent independently chooses when to participate
throughout the algorithm and the specific subset of its neighbourhood with
which it will cooperate at any given moment. When an agent chooses to take
part, it undergoes multiple local updates before conveying its outcomes to the
sub-sampled neighbourhood. Under this setup, we prove that the resulting
asynchronous diffusion strategy is stable in the mean-square error sense and
provide performance guarantees specifically for the federated learning setting.
We illustrate the findings with numerical simulations.

我们研究了一个异步操作的代理网络，旨在发现适合个体本地数据集的理想全局模型。我们的假设是每个代理根据自身算法独立选择何时参与，并在任何给定时刻选择与其合作的特定子集。在该设置下，我们证明了异步扩散策略在均方误差意义下是稳定的，并针对联邦学习设置提供性能保证。我们通过数值模拟进行了实证研究。

异步扩散学习中的代理子抽样和本地更新

Asynchronous Diffusion Learning with Agent Subsampling and Local Updates

While prior domain generalization (DG) benchmarks consider train-test dataset
heterogeneity, we evaluate Federated DG which introduces federated learning
(FL) specific challenges. Additionally, we explore domain-based heterogeneity
in clients' local datasets - a realistic Federated DG scenario. Prior Federated
DG evaluations are limited in terms of the number or heterogeneity of clients
and dataset diversity. To address this gap, we propose an Federated DG
benchmark methodology that enables control of the number and heterogeneity of
clients and provides metrics for dataset difficulty. We then apply our
methodology to evaluate 13 Federated DG methods, which include centralized DG
methods adapted to the FL context, FL methods that handle client heterogeneity,
and methods designed specifically for Federated DG. Our results suggest that
despite some progress, there remain significant performance gaps in Federated
DG particularly when evaluating with a large number of clients, high client
heterogeneity, or more realistic datasets. Please check our extendable
benchmark code here: this https URL

本研究提出了一个可控制客户数量和异质性的联邦迁移学习基准测试方法，并应用于 13 种联邦迁移学习方法的评估，结果表明在客户数量多、客户异质性高或更现实的数据集下，联邦迁移学习的性能仍然存在显著差异。