  Federated learning has evolved to improve a single global model under data
heterogeneity (as a curse) or to develop multiple personalized models using
data heterogeneity (as a blessing). However, little research has considered
both directions simultaneously. In this paper, we first investigate the
relationship between them by analyzing Federated Averaging at the client level
and determine that a better federated global model performance does not
constantly improve personalization. To elucidate the cause of this
personalization performance degradation problem, we decompose the entire
network into the body (extractor), which is related to universality, and the
head (classifier), which is related to personalization. We then point out that
this problem stems from training the head. Based on this observation, we
propose a novel federated learning algorithm, coined FedBABU, which only
updates the body of the model during federated training (i.e., the head is
randomly initialized and never updated), and the head is fine-tuned for
personalization during the evaluation process. Extensive experiments show
consistent performance improvements and an efficient personalization of
FedBABU. The code is available at https://github.com/jhoon-oh/FedBABU.
