When Do Curricula Work in Federated Learning?

Saeed Vahidian, Sreevatsank Kadaveru, Woonjoon Baek, Weijia Wang, Vyacheslav Kungurtsev, Chen Chen, Mubarak Shah, Bill Lin; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 5084-5094

An oft-cited open problem of federated learning is the existence of data heterogeneity among clients. One path- way to understanding the drastic accuracy drop in feder- ated learning is by scrutinizing the behavior of the clients' deep models on data with different levels of "difficulty", which has been left unaddressed. In this paper, we investi- gate a different and rarely studied dimension of FL: ordered learning. Specifically, we aim to investigate how ordered learning principles can contribute to alleviating the hetero- geneity effects in FL. We present theoretical analysis and conduct extensive empirical studies on the efficacy of or- derings spanning three kinds of learning: curriculum, anti- curriculum, and random curriculum. We find that curricu- lum learning largely alleviates non-IIDness. Interestingly, the more disparate the data distributions across clients the more they benefit from ordered learning. We provide analysis explaining this phenomenon, specifically indicating how curriculum training appears to make the objective land- scape progressively less convex, suggesting fast converging iterations at the beginning of the training procedure. We derive quantitative results of convergence for both convex and nonconvex objectives by modeling the curriculum train- ing on federated devices as local SGD with locally biased stochastic gradients. Also, inspired by ordered learning, we propose a novel client selection technique that benefits from the real-world disparity in the clients. Our proposed approach to client selection has a synergic effect when applied together with ordered learning in FL.