Federated_learning_with_differential_privacy：使用PyTorch通过各种神经网络和svm的基于差异优先级的联合学习框架-源码

[{"title":"（ 31 个子文件 33.57MB ） Federated_learning_with_differential_privacy：使用PyTorch通过各种神经网络和svm的基于差异优先级的联合学习框架-源码","children":[{"title":"Federated_learning_with_differential_privacy-master","children":[{"title":"local","children":[{"title":"events.out.tfevents.1612356994.DESKTOP-U8MK2VQ <span style='color:#111;'> 31.02KB </span>","children":null,"spread":false}],"spread":true},{"title":"dataset","children":[{"title":"ADULT.dat <span style='color:#111;'> 11.60MB </span>","children":null,"spread":false},{"title":"mnist","children":[{"title":"MNIST","children":[{"title":"raw","children":[{"title":"train-images-idx3-ubyte.gz <span style='color:#111;'> 9.45MB </span>","children":null,"spread":false},{"title":"train-labels-idx1-ubyte <span style='color:#111;'> 58.60KB </span>","children":null,"spread":false},{"title":"train-images-idx3-ubyte <span style='color:#111;'> 44.86MB </span>","children":null,"spread":false},{"title":"train-labels-idx1-ubyte.gz <span style='color:#111;'> 28.20KB </span>","children":null,"spread":false},{"title":"t10k-labels-idx1-ubyte <span style='color:#111;'> 9.77KB </span>","children":null,"spread":false},{"title":"t10k-labels-idx1-ubyte.gz <span style='color:#111;'> 4.44KB </span>","children":null,"spread":false},{"title":"t10k-images-idx3-ubyte.gz <span style='color:#111;'> 1.57MB </span>","children":null,"spread":false},{"title":"t10k-images-idx3-ubyte <span style='color:#111;'> 7.48MB </span>","children":null,"spread":false}],"spread":true},{"title":"processed","children":[{"title":"training.pt <span style='color:#111;'> 45.32MB </span>","children":null,"spread":false},{"title":"test.pt <span style='color:#111;'> 7.55MB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}],"spread":true},{"title":"Noise_add.py <span style='color:#111;'> 1.31KB </span>","children":null,"spread":false},{"title":"Calculate.py <span style='color:#111;'> 5.02KB </span>","children":null,"spread":false},{"title":"FedNets.py <span style='color:#111;'> 4.89KB </span>","children":null,"spread":false},{"title":"options.py <span style='color:#111;'> 2.70KB </span>","children":null,"spread":false},{"title":"README.md <span style='color:#111;'> 1.03KB </span>","children":null,"spread":false},{"title":"Privacy.py <span style='color:#111;'> 2.07KB </span>","children":null,"spread":false},{"title":"__pycache__","children":[{"title":"Calculate.cpython-37.pyc <span style='color:#111;'> 4.01KB </span>","children":null,"spread":false},{"title":"FedNets.cpython-37.pyc <span style='color:#111;'> 4.79KB </span>","children":null,"spread":false},{"title":"Privacy.cpython-37.pyc <span style='color:#111;'> 1.71KB </span>","children":null,"spread":false},{"title":"options.cpython-37.pyc <span style='color:#111;'> 1.99KB </span>","children":null,"spread":false},{"title":"Update.cpython-37.pyc <span style='color:#111;'> 3.16KB </span>","children":null,"spread":false},{"title":"Noise_add.cpython-37.pyc <span style='color:#111;'> 1.35KB </span>","children":null,"spread":false},{"title":"sampling.cpython-37.pyc <span style='color:#111;'> 3.90KB </span>","children":null,"spread":false},{"title":"averaging.cpython-37.pyc <span style='color:#111;'> 1.60KB </span>","children":null,"spread":false}],"spread":true},{"title":"Fed_Svm.py <span style='color:#111;'> 10.49KB </span>","children":null,"spread":false},{"title":"Fed_NN.py <span style='color:#111;'> 16.47KB </span>","children":null,"spread":false},{"title":"averaging.py <span style='color:#111;'> 1.85KB </span>","children":null,"spread":false},{"title":"Update.py <span style='color:#111;'> 3.65KB </span>","children":null,"spread":false},{"title":"sampling.py <span style='color:#111;'> 4.48KB </span>","children":null,"spread":false}],"spread":false}],"spread":true}]