Figure 2.

Illustration for the motivation of FLIT

We assume two clients as A and B, and the local data on these clients do not share the same distribution as the global one. Local models trained on biased local data will overfit the majority groups of data and underfit others. FLIT measures each sample’s prediction confidence and puts more weight on the uncertain data. As a result, the local data distribution will be better aligned to the global one, and the trained local models will also be more consistent with each other.