  In federated learning (FL), a cluster of local clients are chaired under the
coordination of the global server and cooperatively train one model with
privacy protection. Due to the multiple local updates and the isolated non-iid
dataset, clients are prone to overfit into their own optima, which extremely
deviates from the global objective and significantly undermines the
performance. Most previous works only focus on enhancing the consistency
between the local and global objectives to alleviate this prejudicial client
drifts from the perspective of the optimization view, whose performance would
be prominently deteriorated on the high heterogeneity. In this work, we propose
a novel and general algorithm {\ttfamily FedSMOO} by jointly considering the
optimization and generalization targets to efficiently improve the performance
in FL. Concretely, {\ttfamily FedSMOO} adopts a dynamic regularizer to
guarantee the local optima towards the global objective, which is meanwhile
revised by the global Sharpness Aware Minimization (SAM) optimizer to search
for the consistent flat minima. Our theoretical analysis indicates that
{\ttfamily FedSMOO} achieves fast $\mathcal{O}(1/T)$ convergence rate with low
generalization bound. Extensive numerical studies are conducted on the
real-world dataset to verify its peerless efficiency and excellent generality.
