Confidence Is Not a Vote
TL;DR for operators When several institutions train one shared model without pooling their raw data, the server still has to decide how much influence each institution receives. Sample count is the conventional signal: sites with more training examples contribute more heavily to the combined model. The paper examined here adds two possible signals: how uncertain each local model appears to be, and how well it performs on local validation data. In a simulated three-client infant-movement task, federated training raised aggregate accuracy by more than seven percentage points over institutions training independently. The best federated models reached 0.8603–0.8638 accuracy versus 0.8685 for centralized training. ...