Final_Project_Federated_Learning_with_Encrypted_Gradient_Aggregation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Final Project: Federated Learning with Encrypted Gradient Aggregation.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "TpTQi2J3jOzg",
        "colab_type": "code",
        "outputId": "83b7101c-e8d1-4f25-e6c7-95f1094ba661",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        }
      },
      "source": [
        "!pip install syft"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: syft in /usr/local/lib/python3.6/dist-packages (0.1.21a1)\n",
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            "Requirement already satisfied: flask-socketio>=3.3.2 in /usr/local/lib/python3.6/dist-packages (from syft) (4.1.0)\n",
            "Requirement already satisfied: tf-encrypted>=0.5.4 in /usr/local/lib/python3.6/dist-packages (from syft) (0.5.7)\n",
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            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow<2,>=1.12.0->tf-encrypted>=0.5.4->syft) (0.8.0)\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r207d4QTjTrA",
        "colab_type": "code",
        "outputId": "5acab4c3-fd7c-4d3c-a28d-8d1e6f40a67b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        }
      },
      "source": [
        "import torch as th\n",
        "from torch import nn, optim\n",
        "import syft as sy\n",
        "hook = sy.TorchHook(th)\n",
        "alice = sy.VirtualWorker(hook, id=\"alice\")\n",
        "bob = sy.VirtualWorker(hook, id=\"bob\")\n",
        "secure_worker = sy.VirtualWorker(hook, id=\"secure_worker\")\n",
        "alice.add_workers([bob, secure_worker])\n",
        "bob.add_workers([alice, secure_worker])\n",
        "secure_worker.add_workers([bob, alice])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0725 06:44:54.146835 139711681836928 secure_random.py:26] Falling back to insecure randomness since the required custom op could not be found for the installed version of TensorFlow. Fix this by compiling custom ops. Missing file was '/usr/local/lib/python3.6/dist-packages/tf_encrypted/operations/secure_random/secure_random_module_tf_1.14.0.so'\n",
            "W0725 06:44:54.163732 139711681836928 deprecation_wrapper.py:119] From /usr/local/lib/python3.6/dist-packages/tf_encrypted/session.py:26: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "W0725 06:44:54.801971 139711681836928 base.py:628] Worker bob already exists. Replacing old worker which could cause                     unexpected behavior\n",
            "W0725 06:44:54.802900 139711681836928 base.py:628] Worker secure_worker already exists. Replacing old worker which could cause                     unexpected behavior\n",
            "W0725 06:44:54.804019 139711681836928 base.py:628] Worker alice already exists. Replacing old worker which could cause                     unexpected behavior\n",
            "W0725 06:44:54.807960 139711681836928 base.py:628] Worker secure_worker already exists. Replacing old worker which could cause                     unexpected behavior\n",
            "W0725 06:44:54.810928 139711681836928 base.py:628] Worker bob already exists. Replacing old worker which could cause                     unexpected behavior\n",
            "W0725 06:44:54.813133 139711681836928 base.py:628] Worker alice already exists. Replacing old worker which could cause                     unexpected behavior\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<VirtualWorker id:secure_worker #objects:0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hHIMn_Qmj3k8",
        "colab_type": "code",
        "outputId": "2d6206b0-65a2-4b58-964e-0598210e9a2d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "alice.clear_objects()\n",
        "bob.clear_objects()\n",
        "secure_worker.clear_objects()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<VirtualWorker id:secure_worker #objects:0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzWj6yOjjThk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = th.tensor([[1.,1],[0,1],[1,0],[0,0]], requires_grad = True)\n",
        "target = th.tensor([[1.],[1],[0],[0]], requires_grad = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lDMNYLJLjTZ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "alice_data = data[0:2].send(alice)\n",
        "alice_target = target[0:2].send(alice)\n",
        "\n",
        "bob_data = data[2:].send(bob)\n",
        "bob_target = target[2:].send(bob)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye0doYLrkUEb",
        "colab_type": "code",
        "outputId": "5aaf31e8-ab7e-4669-a909-8615900933cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "bob._objects"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{80866162505: tensor([[0.],\n",
              "         [0.]], requires_grad=True), 93288834077: tensor([[1., 0.],\n",
              "         [0., 0.]], requires_grad=True)}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HBAHIYXkkTae",
        "colab_type": "code",
        "outputId": "cc2d8821-0b48-47e6-9878-eb7399bb8cea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "alice._objects"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{13504270611: tensor([[1., 1.],\n",
              "         [0., 1.]], requires_grad=True), 95383268194: tensor([[1.],\n",
              "         [1.]], requires_grad=True)}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TqNRXAcsjTW3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = nn.Linear(2,1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HXqGor2BjTJ_",
        "colab_type": "code",
        "outputId": "3fcb03a2-84c6-4f3d-a137-c65f6df2b267",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        }
      },
      "source": [
        "for i in range(10):\n",
        "  alice_model = model.copy().send(alice)\n",
        "  bob_model = model.copy().send(bob)\n",
        "  \n",
        "  alice_opt = optim.SGD(params=alice_model.parameters(), lr=0.01)\n",
        "  bob_opt = optim.SGD(params=bob_model.parameters(), lr=0.01)\n",
        "  \n",
        "  #params = [list(bob_model.parameters()), list(alice_model.parameters())]\n",
        "  \n",
        "  for worker_iter in range(5):\n",
        "    alice_opt.zero_grad()\n",
        "    alice_pred = alice_model(alice_data)\n",
        "    alice_loss = ((alice_pred - alice_target) ** 2).sum()\n",
        "    alice_loss.backward()\n",
        "    alice_opt.step()\n",
        "    alice_loss = alice_loss.get().data\n",
        "    \n",
        "    bob_opt.zero_grad()\n",
        "    bob_pred = bob_model(bob_data)\n",
        "    bob_loss = ((bob_pred - bob_target) ** 2).sum()\n",
        "    bob_loss.backward()\n",
        "    bob_opt.step()\n",
        "    bob_loss = bob_loss.get().data\n",
        "  \n",
        "  '''for remote_index in range(len(compute_nodes)):\n",
        "    \n",
        "    if(remote_index == 0):\n",
        "      data, target = bob_data, bob_target\n",
        "    else:\n",
        "      data, target = alice_data, alice_target\n",
        "      \n",
        "    models[remote_index] = update(data, target, models[remote_index], optimizers[remote_index])\n",
        "    \n",
        "  new_params = list()\n",
        "  for param_i in range(len(params[0])):\n",
        "    worker_param = list()\n",
        "    for index in range(len(compute_nodes)):\n",
        "      encrypted_param = params[index][param_i].copy().fix_prec().share(bob, alice, crypto_provider = secure_worker)\n",
        "      worker_param.append(encrypted_param.get())\n",
        "      \n",
        "    new_param = (worker_param[0] + worker_param[1]).get().float_prec()/2\n",
        "    new_params.append(new_param)'''\n",
        "  \n",
        "  alice_wt = alice_model.weight.data.fix_precision().share(bob, alice, crypto_provider=secure_worker).get()\n",
        "  bob_wt = bob_model.weight.data.fix_precision().share(bob, alice, crypto_provider = secure_worker).get()\n",
        "  alice_bias = alice_model.bias.data.fix_precision().share(bob, alice, crypto_provider = secure_worker).get() \n",
        "  bob_bias = bob_model.bias.data.fix_precision().share(bob, alice, crypto_provider = secure_worker).get()\n",
        "  \n",
        "  with th.no_grad():\n",
        "    model.weight.set_((( alice_wt + bob_wt )/2).get().float_prec())\n",
        "    model.bias.set_((( alice_bias + bob_bias)/2).get().float_prec())\n",
        "    \n",
        "    '''for index in range(len(compute_nodes)):\n",
        "      for param_i in range(len(params[index])):\n",
        "        params[index][param_i].set_(new_params[param_i])'''\n",
        "    \n",
        "  print(\"Alice's loss:\" + str(alice_loss) + \"  Bob's loss:\" + str(bob_loss))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-16-beef2a0cc60c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     42\u001b[0m     new_params.append(new_param)'''\n\u001b[1;32m     43\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m   \u001b[0malice_wt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malice_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_precision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrypto_provider\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msecure_worker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     45\u001b[0m   \u001b[0mbob_wt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbob_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_precision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrypto_provider\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msecure_worker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m   \u001b[0malice_bias\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malice_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbias\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfix_precision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshare\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbob\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malice\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcrypto_provider\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msecure_worker\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py\u001b[0m in \u001b[0;36mfix_prec\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    645\u001b[0m         \u001b[0mprec_fractional\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"precision_fractional\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0mmax_precision\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_maximum_precision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 647\u001b[0;31m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_requires_large_precision\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmax_precision\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbase\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprec_fractional\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    648\u001b[0m             return (\n\u001b[1;32m    649\u001b[0m                 \u001b[0msyft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mLargePrecisionTensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/tensors/interpreters/native.py\u001b[0m in \u001b[0;36m_requires_large_precision\u001b[0;34m(self, max_precision, base, precision_fractional)\u001b[0m\n\u001b[1;32m    670\u001b[0m         \u001b[0;31m# We need to use NumPy here as log2 is not yet implemented for LongTensor PyTorch objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    671\u001b[0m         return np.any(\n\u001b[0;32m--> 672\u001b[0;31m             \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlog2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdetach\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mbase_fractional\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0mmax_precision\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    673\u001b[0m         )\n\u001b[1;32m    674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook.py\u001b[0m in \u001b[0;36moverloaded_native_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    675\u001b[0m                 \u001b[0;31m# Send the new command to the appropriate class and get the response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    676\u001b[0m                 \u001b[0mmethod\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_self\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod_name\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 677\u001b[0;31m                 \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mnew_args\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mnew_kwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    678\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    679\u001b[0m                 \u001b[0;31m# For inplace methods, just directly return self\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook.py\u001b[0m in \u001b[0;36moverloaded_pointer_method\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    511\u001b[0m             \u001b[0mcommand\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    512\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 513\u001b[0;31m             \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocation\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_command\u001b[0;34m(self, recipient, message, return_ids)\u001b[0m\n\u001b[1;32m    425\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    426\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 427\u001b[0;31m             \u001b[0mret_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcodes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMSGTYPE\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCMD\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrecipient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    428\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mResponseSignatureError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    429\u001b[0m             \u001b[0mret_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/base.py\u001b[0m in \u001b[0;36msend_msg\u001b[0;34m(self, msg_type, message, location)\u001b[0m\n\u001b[1;32m    221\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    222\u001b[0m         \u001b[0;31m# Step 2: send the message and wait for a response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 223\u001b[0;31m         \u001b[0mbin_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbin_message\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    224\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    225\u001b[0m         \u001b[0;31m# Step 3: deserialize the response\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/virtual.py\u001b[0m in \u001b[0;36m_send_msg\u001b[0;34m(self, message, location)\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mVirtualWorker\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseWorker\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mFederatedClient\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_send_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mBaseWorker\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlocation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_recv_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recv_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/virtual.py\u001b[0m in \u001b[0;36m_recv_msg\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_recv_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mbin\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrecv_msg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/base.py\u001b[0m in \u001b[0;36mrecv_msg\u001b[0;34m(self, bin_message)\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"worker {self} received {sy.codes.code2MSGTYPE[msg_type]} {contents}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0;31m# Step 1: route message to appropriate function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_message_router\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmsg_type\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcontents\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    256\u001b[0m         \u001b[0;31m# Step 2: Serialize the message to simple python objects\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/workers/base.py\u001b[0m in \u001b[0;36mexecute_command\u001b[0;34m(self, message)\u001b[0m\n\u001b[1;32m    391\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    392\u001b[0m                 response = sy.frameworks.torch.hook_args.register_response(\n\u001b[0;32m--> 393\u001b[0;31m                     \u001b[0mcommand_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturn_ids\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    394\u001b[0m                 )\n\u001b[1;32m    395\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mresponse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook_args.py\u001b[0m in \u001b[0;36mregister_response\u001b[0;34m(attr, response, response_ids, owner)\u001b[0m\n\u001b[1;32m    657\u001b[0m         \u001b[0mregister_response_function\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregister_response_functions\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mattr_id\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    658\u001b[0m         \u001b[0;31m# Try running it\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 659\u001b[0;31m         \u001b[0mnew_response\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mregister_response_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mresponse_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresponse_ids\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mowner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    660\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    661\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mIndexError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAssertionError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# Update the function in cas of an error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x, **kwargs)\u001b[0m\n\u001b[1;32m    757\u001b[0m         \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmany_fold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    758\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 759\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook_args.py\u001b[0m in \u001b[0;36mtwo_fold\u001b[0;34m(lambdas, args, **kwargs)\u001b[0m\n\u001b[1;32m    514\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    515\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mtwo_fold\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlambdas\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlambdas\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    517\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook_args.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(i, **kwargs)\u001b[0m\n\u001b[1;32m    735\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# if the rule is a list or tuple.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    736\u001b[0m         \u001b[0;31m# Last if not, rule is probably == 1 so use type to return the right transformation.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 737\u001b[0;31m         \u001b[0;32melse\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mregister_tensor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    738\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrules\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# And do this for all the responses / rules provided\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    739\u001b[0m     ]\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/syft/frameworks/torch/hook/hook_args.py\u001b[0m in \u001b[0;36mregister_tensor\u001b[0;34m(tensor, owner, response_ids)\u001b[0m\n\u001b[1;32m    706\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0meach\u001b[0m \u001b[0mid\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mpop\u001b[0m \u001b[0mout\u001b[0m \u001b[0mwhen\u001b[0m \u001b[0mneeded\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    707\u001b[0m     \"\"\"\n\u001b[0;32m--> 708\u001b[0;31m     \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mowner\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mowner\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    709\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    710\u001b[0m         \u001b[0mtensor\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mid\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mresponse_ids\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'owner'"
          ]
        }
      ]
    }
  ]
}