Motivated by the above findings, we propose FedCCFA, a federated learning framework with classifier clustering and feature alignment.

Writing testerror(test) w and w0 ( 1 )err(w0))].

GivenatrainingdatasetD =(X,y)offeaturesandcorresponding labels from {1, ..., T} classes,D is partitioned recursively to two subsets, according to classes, at each tree level until reaching leaf nodes with data from only one class. More concretely, initially, feature vectors for all samples are obtained (using a NN), then a class prototype is generated by averaging the feature vectors belonging to the same class for all classes.

We address reviewer 1, 2 and 3 as R1, R2, R3. R1-(5): We use 25%, 75% quartiles for the shaded areas, see line 147 in the paper. R2 - Originality: Thank you for pointing us to additional relevant related work: we have added citations. We provide additional results on CINIC-10 (top figure, left). We use 50 MC dropout samples, acquisition size 10 and 6 trials.

A central challenge in training classification models in the real-world federated system is learning with non-IID data. To cope with this, most of the existing works involve enforcing regularization in local optimization or improving the model aggregation scheme at the server.

Figure 8 shows the results.