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

Before Change

    // Predict
    for (i, c) in enumerate(clients):
        print("Train", mean_square_error(c.predict(X[i]), y[i]))
        print("Test", mean_square_error(c.predict(X_test), y_test))

    // Each client sends its own model to the next one, in a RING protocol,

After Change

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

    // Instantiate Alice, Bob and Carol.
    // Each client gets the public key at creation
    clients = []
    for i in range(n_clients):
        clients.append(Client(names[i], X[i], y[i], server.pubkey))

Instances