Concrete is really impressive and permissively licensed. The ML library has a FHE version of (a subset of) scikit-learn, which I honestly thought I’d see in another 5+ years. Like look at this example:<p><pre><code> # Now we train in the clear and quantize the weights
model = LogisticRegression(n_bits=8)
model.fit(X_train, y_train)
# We can simulate the predictions in the clear
y_pred_clear = model.predict(X_test)
# We then compile on a representative set
model.compile(X_train)
# Finally we run the inference on encrypted inputs !
y_pred_fhe = model.predict(X_test, fhe="execute")
print("In clear :", y_pred_clear)
print("In FHE :", y_pred_fhe)
print(f"Similarity: {int((y_pred_fhe == y_pred_clear).mean()*100)}%")
</code></pre>
There’s some ways to go on performance, but the ergonomics of using FHE are already pretty good!