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Federated Learning (FL) is an emerging paradigm that allows a model to betrained across a number of participants without sharing data. Recent works havebegun to consider the effects of using pre-trained models as an initializationpoint for existing FL algorithms; however, these approaches ignore the vastbody of efficient transfer learning literature from the centralized learningsetting. Here we revisit the problem of FL from a pre-trained model consideredin prior work and expand it to a set of computer vision transfer learningproblems. We first observe that simply fitting a linear classification head canbe 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 exactlyand orders of magnitude more efficiently than existing proposals, whileobtaining strong performance. Finally, we demonstrate that using a two-phaseapproach of obtaining the classifier and then fine-tuning the model can yieldrapid convergence and improved generalization in the federated setting. Wedemonstrate the potential our method has to reduce communication and computecosts while achieving better model performance.

Guiding The Last Layer in Federated Learning with Pre-Trained Models