Convergence Guarantees for Sequential Federated Learning on Heterogeneous Data

This paper establishes sharp convergence guarantees for sequential federated learning (SFL) on heterogeneous data, including both upper and lower bounds. It also compares the convergence of SFL with parallel federated learning (PFL), showing that SFL outperforms PFL when the level of heterogeneity is relatively high.