  Federated learning (FL) is a promising strategy for performing
privacy-preserving, distributed learning with a network of clients (i.e., edge
devices). However, the data distribution among clients is often non-IID in
nature, making efficient optimization difficult. To alleviate this issue, many
FL algorithms focus on mitigating the effects of data heterogeneity across
clients by introducing a variety of proximal terms, some incurring considerable
compute and/or memory overheads, to restrain local updates with respect to the
global model. Instead, we consider rethinking solutions to data heterogeneity
in FL with a focus on local learning generality rather than proximal
restriction. To this end, we first present a systematic study informed by
second-order indicators to better understand algorithm effectiveness in FL.
Interestingly, we find that standard regularization methods are surprisingly
strong performers in mitigating data heterogeneity effects. Based on our
findings, we further propose a simple and effective method, FedAlign, to
overcome data heterogeneity and the pitfalls of previous methods. FedAlign
achieves competitive accuracy with state-of-the-art FL methods across a variety
of settings while minimizing computation and memory overhead. Code is available
at https://github.com/mmendiet/FedAlign
