1bcbc90debe300740369b1151b3f1b8523289f91,examples/federated_learning_with_encryption.py,,federated_learning,#,196

Before Change

          "training only on own local data:")
    for c in clients:
        c.fit(n_iter, eta)
        y_pred = c.predict(X_test)
        mse = mean_square_error(y_pred, y_test)
        print("{:s}:\t{:.2f}".format(c.name, mse))

    // The federated learning with gradient descent
    print("Running distributed gradient aggregation for {:d} iterations"
          .format(n_iter))

After Change

    // Instantiate the server and generate private and public keys
    // NOTE: using smaller keys sizes wouldn"t be cryptographically safe
    server = Server(key_length=config["key_length"])

    // Instantiate the clients.
    // Each client gets the public key at creation and its own local dataset

Instances