  The statistical heterogeneity of the non-independent and identically
distributed (non-IID) data in local clients significantly limits the
performance of federated learning. Previous attempts like FedProx, SCAFFOLD,
MOON, FedNova and FedDyn resort to an optimization perspective, which requires
an auxiliary term or re-weights local updates to calibrate the learning bias or
the objective inconsistency. However, in addition to previous explorations for
improvement in federated averaging, our analysis shows that another critical
bottleneck is the poorer optima of client models in more heterogeneous
conditions. We thus introduce a data-driven approach called FedSkip to improve
the client optima by periodically skipping federated averaging and scattering
local models to the cross devices. We provide theoretical analysis of the
possible benefit from FedSkip and conduct extensive experiments on a range of
datasets to demonstrate that FedSkip achieves much higher accuracy, better
aggregation efficiency and competing communication efficiency. Source code is
available at: https://github.com/MediaBrain-SJTU/FedSkip.
