The number of communication rounds is 100. The total number of clients is 4. A two-layer CNN architecture is used as the backbone model here. The local training batch size is 32. An SGD optimizer with a weight decay rate 5e-4 and a learning rate 0.01 is used. There are 20 clients in total.

We make use commute time'JWMNP' as the target The California datacenter has access to all of the features. The Texas datacenter has access to all but'AGEP', 'SCHL '. For each method that we test, we run 20 trials to form 95% confidence intervals. Optimized-Naive-Collab, described in Section 6. As the Schur complement is also p.s.d.

Figure 7: Actual example of how an anchor function impacts the generated solution. In this section, we provide additional experimental details and results for the experiments in Section 3. We include additional details for anchoring (Appendix A.1), the availability heuristic (Appendix A.3), Filtering prompts for longer canonical solutions. However, all components of the prompts from Section 3.3.2 We plot the analogous add-var results in Figure 10 and include full numerical results in Table 7. In this section, we augment Section 3.3.3

This is referred to as DOC-Feat in [24]. COT uses the empirical estimator of the Earth Mover's Distance between labels from the source domain and softmax outputs of samples from the target A.2 Dataset Details In this section, we provide additional details about the datasets used in our benchmark study. Overall, we obtain 5 datasets (i.e., CIFAR10v1, CIF AR100 Similar to CIFAR10, we use the original CIFAR100 set as the source dataset. Overall, we obtain 3 different domains. Overall, we obtain 3 different domains.

The number of communication rounds is 100. The total number of clients is 4. A two-layer CNN architecture is used as the backbone model here. The local training batch size is 32. An SGD optimizer with a weight decay rate 5e-4 and a learning rate 0.01 is used. There are 20 clients in total.

The authors would like to thank the reviewers for their thoughtful comments. We have replaced the calibration experiment. Figure 1: ALICE score calibration of ResNet32 trained on CIFAR10. At 50 epochs we reach max validation accuracy. Full experimental details are in the final version.