The Smallest Client Needs More Than a Better Backbone
TL;DR for operators A pretrained model can be well suited to a text-classification task and still perform poorly for a participant with almost no labeled data. In experiments reported by Naseer and colleagues,1 removing federated aggregation reversed the pretrained models’ worst-client advantage across all five tested configurations. Personalization recovered only 4–12% of the performance gap between local-only and shared-model training in three domain-matched settings. ...