  Federated learning (FL) is a distributed method to train a global model over
a set of local clients while keeping data localized. It reduces the risks of
privacy and security but faces important challenges including expensive
communication costs and client drift issues. To address these issues, we
propose FedElasticNet, a communication-efficient and drift-robust FL framework
leveraging the elastic net. It repurposes two types of the elastic net
regularizers (i.e., $\ell_1$ and $\ell_2$ penalties on the local model
updates): (1) the $\ell_1$-norm regularizer sparsifies the local updates to
reduce the communication costs and (2) the $\ell_2$-norm regularizer resolves
the client drift problem by limiting the impact of drifting local updates due
to data heterogeneity. FedElasticNet is a general framework for FL; hence,
without additional costs, it can be integrated into prior FL techniques, e.g.,
FedAvg, FedProx, SCAFFOLD, and FedDyn. We show that our framework effectively
resolves the communication cost and client drift problems simultaneously.
