8184b9fdf51e3f75835fe1f2d56c294d16686241,examples/federated_learning_with_encryption.py,,,#,162

Before Change

    // We need a baseline to understand how good is any future prediction
    print("Compute a baseline: the mean of all training data")
    print("Baseline at test time:", mean_square_error(np.mean(y[0]), y_test))

    // Instantiate Alice, Bob and Carol.
    // Each client gets the public key at creation

After Change

        // Take gradient steps
        clients[0].gradient_step(aggr)
        clients[1].gradient_step(aggr)
        clients[2].gradient_step(aggr)

    for (i, c) in enumerate(clients):
        y_pred = c.predict(c.X)
        print(mean_square_error(y_pred, c.y))

Instances