server_weights <float32[784,10],float32[10]>@SERVER mean_client_wieghts <float32[784,10],float32[10]>@SERVER ## Starting... ## Preprocessing the federated_train_data ## Declaring the model ## Declaring the federated algorithm tf_dataset_type <x=float32[?,784],y=int32[?,1]>* model_weights_type <float32[784,10],float32[10]> server_state type <class 'list'> <class 'numpy.ndarray'> FINISHEEED server_state[1] [ 5.4024562e-04 -1.6237081e-03 6.2275940e-04 1.4378619e-05 -3.4344319e-04 4.4040685e-04 -6.7906491e-05 -3.0773325e-04 1.3574951e-04 5.8925571e-04] server2_state[1] [ 5.8093132e-04 -2.8670396e-05 1.1061553e-04 -1.5197636e-04 -4.6668845e-04 2.7149473e-04 -1.8408171e-04 5.8942172e-05 -3.8304061e-04 1.9247324e-04] merged_state[1] [ 5.6058844e-04 -8.2618924e-04 3.6668745e-04 -6.8798872e-05 -4.0506583e-04 3.5595079e-04 -1.2599411e-04 -1.2439553e-04 -1.2364556e-04 3.9086447e-04] ## Evaluation of the model server_weights <float32[784,10],float32[10]>@SERVER mean_client_wieghts <float32[784,10],float32[10]>@SERVER ## Starting... ## Preprocessing the federated_train_data ## Declaring the model ## Declaring the federated algorithm tf_dataset_type <x=float32[?,784],y=int32[?,1]>* model_weights_type <float32[784,10],float32[10]> server_state type <class 'list'> <class 'numpy.ndarray'> FINISHEEED server_state[1] [ 5.40245732e-04 -1.62370806e-03 6.22759399e-04 1.43785965e-05 -3.43443331e-04 4.40406700e-04 -6.79065852e-05 -3.07733397e-04 1.35749448e-04 5.89255709e-04] server2_state[1] [ 5.8093149e-04 -2.8670451e-05 1.1061558e-04 -1.5197645e-04 -4.6668845e-04 2.7149462e-04 -1.8408173e-04 5.8942172e-05 -3.8304055e-04 1.9247322e-04] merged_state[1] [ 5.6058861e-04 -8.2618924e-04 3.6668748e-04 -6.8798923e-05 -4.0506589e-04 3.5595067e-04 -1.2599415e-04 -1.2439561e-04 -1.2364556e-04 3.9086447e-04] ## Evaluation of the model server_weights <float32[784,10],float32[10]>@SERVER mean_client_wieghts <float32[784,10],float32[10]>@SERVER server_weights <float32[784,10],float32[10]>@SERVER mean_client_wieghts <float32[784,10],float32[10]>@SERVER ## Starting... ## Preprocessing the federated_train_data ## Declaring the model ## Declaring the federated algorithm tf_dataset_type <x=float32[?,784],y=int32[?,1]>* model_weights_type <float32[784,10],float32[10]> server_state type <class 'list'> <class 'numpy.ndarray'> FINISHEEED server_state[1] [ 5.4024585e-04 -1.6237078e-03 6.2275951e-04 1.4378643e-05 -3.4344324e-04 4.4040682e-04 -6.7906469e-05 -3.0773322e-04 1.3574972e-04 5.8925588e-04] server2_state[1] [ 5.80931432e-04 -2.86704162e-05 1.10615605e-04 -1.51976506e-04 -4.66688391e-04 2.71494675e-04 -1.84081669e-04 5.89420633e-05 -3.83040548e-04 1.92473250e-04] merged_state[1] [ 5.6058867e-04 -8.2618912e-04 3.6668757e-04 -6.8798930e-05 -4.0506583e-04 3.5595073e-04 -1.2599406e-04 -1.2439558e-04 -1.2364541e-04 3.9086456e-04] ## Evaluation of the model