Federated Learning (FL) is an emerging paradigm that allows a model to be
trained across a number of participants without sharing data. Recent works have
begun to consider the effects of using pre-trained models as an initialization
point for existing FL algorithms; however, these approaches ignore the vast
body of efficient transfer learning literature from the centralized learning
setting. Here we revisit the problem of FL from a pre-trained model considered
in prior work and expand it to a set of computer vision transfer learning
problems. We first observe that simply fitting a linear classification head can
be efficient and effective in many cases. We then show that in the FL setting,
fitting a classifier using the Nearest Class Means (NCM) can be done exactly
and orders of magnitude more efficiently than existing proposals, while
obtaining strong performance. Finally, we demonstrate that using a two-phase
approach of obtaining the classifier and then fine-tuning the model can yield
rapid convergence and improved generalization in the federated setting. We
demonstrate the potential our method has to reduce communication and compute
costs while achieving better model performance.

通过使用预训练模型作为初始化点，利用最近类平均法编写分类器和使用两阶段方法进行微调，提高了联邦学习的效率和性能。