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.

FL framework, namely FedICON, to capture invariant knowledge among heterogeneous clients and consistently tune the model to adapt to test data.

It also makes no assumptions on the sparseness of the transitions. Our experiments reflect this as well,25 as the transition probabilities are drawn from a uniform distribution with no sparseness assumptions and would be26 more difficult tothan sparse cases.

This is useful for a variety of applications like sports analysis (e.g.

Our first step is to prove the parameterization (Eq. 3) provides local attention after the Note that the weight and bias terms in theaboveformulation (Eq. Assume the position-based function at each head is learned to perform'hard attention' on one of its surrounding positions,i.e., an extreme semi-dynamic attention. To demonstrate this phenomenon, we plot and compare the impacts ofΦc and Φp6 on Φa in the middle and right of Fig. S4 and visualize learned position-based attentionΦp of iRPE in Fig. S5. As seen from Tab. S17, there exist noticeable performance gaps between the models (b, f, g, h) (withoutΦp)and(a,d,e,i)(withΦp). Without adaptiveattention (model (c)),Φp imposes stronger locality onevery layer.

As seen in Figure 1, we have high-resolution processing near the center ofourgaze,i.e.,central and para-central regions, toidentify highly-detailed visual elements such as geometric shapes, and low-level details.