Minor edits to README and requirements.txt

README.md

@@ -4,7 +4,7 @@ Implementation of both hierarchical and vanilla federated learning based on the
 
 Experiments are conducted on MNIST and CIFAR10 datasets. During training, the datasets split are both IID and non-IID. In case of non-IID, the data amongst the users can be split equally or unequally.
 
-Since the purpose of these experiments are to illustrate the effectiveness of the federated learning paradigm, only simple models such as MLP and CNN are used.
+Since the purpose of these experiments is to illustrate the effectiveness of the federated learning paradigm, only simple models such as MLP and CNN are used.
 
 ## Requirements
 Install all the packages from requirements.txt
@@ -81,7 +81,8 @@ The default values for various paramters parsed to the experiment are given in `
 
 * ```--dataset:```  Default: 'mnist'. Options: 'mnist', 'cifar'
 * ```--model:```    Default: 'mlp'. Options: 'mlp', 'cnn'
-* ```--gpu:```      Default: 1 (runs on gpu:0)
+* ```--gpu:```      Default: 1 (runs on gpu:0). Select 0 if using CPU only
+* ```--gpu_id:```	Default: 'cuda:0' (this specifies which GPU to use)
 * ```--epochs:```   Number of rounds of training.
 * ```--lr:```       Learning rate set to 0.01 by default.
 * ```--verbose:```  Detailed log outputs. Activated by default, set to 0 to deactivate.

activate.sh

@@ -1,9 +0,0 @@
-# To activate this environment, use
-#
-conda activate fl_pytorch
-
-
-
-# To deactivate an active environment, use
-#
-#     $ conda deactivate

requirements.txt

@@ -1,4 +1,7 @@
-tqdm==4.39.0 
-numpy==1.15.4
-matplotlib==3.0.1
-tensorboardx==1.4
+Python==3.7.3
+Pytorch==1.2.0
+Torchvision==0.4.0
+Numpympy==1.15.4
+Tensorboardx==1.4
+Matplotlib==3.0.1
+Tqdm==4.39.0