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Data generated at the network edge can be processed locally by leveraging theparadigm of edge computing (EC). Aided by EC, decentralized federated learning(DFL), which overcomes the single-point-of-failure problem in the parameterserver (PS) based federated learning, is becoming a practical and popularapproach for machine learning over distributed data. However, DFL faces twocritical challenges, \ie, system heterogeneity and statistical heterogeneityintroduced by edge devices. To ensure fast convergence with the existence ofslow edge devices, we present an efficient DFL method, termed FedHP, whichintegrates adaptive control of both local updating frequency and networktopology to better support the heterogeneous participants. We establish atheoretical relationship between local updating frequency and network topologyregarding model training performance and obtain a convergence upper bound. Uponthis, we propose an optimization algorithm, that adaptively determines localupdating frequencies and constructs the network topology, so as to speed upconvergence and improve the model accuracy. Evaluation results show that theproposed FedHP can reduce the completion time by about 51% and improve modelaccuracy by at least 5% in heterogeneous scenarios, compared with thebaselines.

Adaptive Configuration for Heterogeneous Participants in Decentralized Federated Learning