Decentralized Federated Learning (DFL) has received significant recent
research attention, capturing settings where both model updates and model
aggregations -- the two key FL processes -- are conducted by the clients. In
this work, we propose Decentralized Sporadic Federated Learning
($\texttt{DSpodFL}$), a DFL methodology which generalizes the notion of
sporadicity in both of these processes, modeling the impact of different forms
of heterogeneity that manifest in realistic DFL settings. $\texttt{DSpodFL}$
unifies many of the prominent decentralized optimization methods, e.g.,
distributed gradient descent (DGD), randomized gossip (RG), and decentralized
federated averaging (DFedAvg), under a single modeling framework. We
analytically characterize the convergence behavior of $\texttt{DSpodFL}$,
showing, among other insights, that we can match a geometric convergence rate
to a finite optimality gap under more general assumptions than in existing
works. Through experiments, we demonstrate that $\texttt{DSpodFL}$ achieves
significantly improved training speeds and robustness to variations in system
parameters compared to the state-of-the-art.

分散式联邦学习的 DSpodFL 方法通过模拟现实环境中的不同异质性形式来泛化间歇性，实现了更快的训练速度和对系统参数变化的鲁棒性。