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

Before Change

    clients.append(Client("Carol", server.pubkey))

    // Each client trains a linear regressor on its own data
    for (i, c) in enumerate(clients):
        c = c.fit(X[i], y[i], n_iter=50, eta=0.05)
        print(c.weights)

    // 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))

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