secure aggregation

Federated learning trains across decentralized devices without pooling data, yet a malicious participant can poison the shared model. What lands is the privacy mechanism plus the outlier-resistant aggregation defenses. Here is the answer.

Federated learning transmits gradients, not raw data. The catch: a gradient is derived from the data, so it carries the data. The signal is knowing why 'we only share gradients' is not a privacy guarantee. Here is the answer.