Secure aggregation isn’t meaningless, but you’re right that it’s the wrong tool for the problem you’re talking about. The right tool is differential privacy. D

Secure aggregation isn’t meaningless, but you’re right that it’s the wrong tool for the problem you’re talking about. The right tool is differential privacy. Differential privacy is exactly meant for this, in fact. Differential privacy adds a certain amount of randomly-generated noise to client inputs. The result is that, statistically speaking, it’s impossible to tell the difference between a model with your data in it and a model without your data in it. Arguably the reason the comic doesn’t mention differential privacy is that it’s neither new nor invented at Google. Or maybe just because it’s not technically part of federated learning. But the “federated learning at scale” paper Google put out mentions it, and says they have implemented DP techniques.