Merged PR 1563: Adapt federated.py for extra privacy metrics

Old federated.py was breaking when enabling additional privacy metrics in the config file. This PR allows to include these extra keys in the client payload communicated to the Server.

NLG_GRU:
[X] Apply DP metrics:  https://aka.ms/amlt?q=hlimq
[X] DP metrics disabled: https://aka.ms/amlt?q=hlimz

core/federated.py

@@ -132,9 +132,15 @@ def _send_train_output(output):
     cs_values = [float(cs_v) for cs_v in output['cs'].values()] # cs dict -- values are flatten in 1d array
     pl_values = [float(output['pl']['weight'])] # pl dict
     gradients = output['pl']['gradients'] # gradients are sent independently
-    values = cs_values + [float(output[key]) for key in output.keys() if key not in ['cs','pl']] + pl_values # reorganizing values in the order expected by the Server
+
+    if len(output.keys()) > 9: # DP metrics
+        ps_values = [float(ps_v) for ps_v in output['ps'].values()]
+        values = cs_values + [float(output[key]) for key in output.keys() if key not in ['cs','pl','ps']] + pl_values + ps_values # reorganizing values in the order expected by the Server
+    else:
+        values = cs_values + [float(output[key]) for key in output.keys() if key not in ['cs','pl']] + pl_values # reorganizing values in the order expected by the Server
 
     # Send data
+    _send(int(len(output.keys())),0) # Warn for number of keys
     _send(values, 0)
     _send_gradients(gradients, 0)
 
@@ -144,17 +150,34 @@ def build_grads_dict(node):
     distributed communication. """
 
     # Initialize tensors
-    keys = ['cs','tl','mg','vg','ng','rg','ns','ts','pl']
-    values = [0.0 for i in range(11)] # initializing tensor shape -- 11 is fixed number of keys expected
+    n_keys = 0
+    n_keys = _recv(n_keys,node)
+    print(n_keys)
+
+    if n_keys == 9:
+        keys = ['cs','tl','mg','vg','ng','rg','ns','ts','pl']
+        values = [0.0 for i in range(11)] # initializing tensor shape -- 11 is fixed number of keys expected
+    elif n_keys == 10:
+        keys = ['cs','tl','mg','vg','ng','rg','ns','ts','pl','ps']
+        values = [0.0 for i in range(15)] # When the privacy metrics are enabled
+    elif n_keys == 11:
+        keys = ['cs','tl','mg','vg','ng','rg','ns','wt','ts','pl','ps']
+        values = [0.0 for i in range(16)] # When the privacy metrics are enabled
 
     # Read data
     values = _recv(values,node)
     grads = _recv_gradients(node)
 
-    # Rebuilding original dictionary
     cs_values = [{key: values.pop(0) for key in ['setup','training','full cost']}] # recreating cs dict
-    pl_values = [{'weight':values.pop(), 'gradients': grads}] # recreating pl dict
-    values_list = cs_values + [values.pop(0) for i in range(7)] + pl_values # 7 is fixed length for remaining items
+    # Rebuilding original dictionary
+    if n_keys == 9:
+        pl_values = [{'weight':values.pop(), 'gradients': grads}] # recreating pl dict
+        values_list = cs_values + [values.pop(0) for i in range(7)] + pl_values # 7 is fixed length for remaining items
+    else:
+        ps_values = [{key: values.pop() for key in ['Practical epsilon (Max leakage)','Words percentage above 9000 word rank','Extracted indices percentage','Dropped clients']}]
+        pl_values = [{'weight':values.pop(), 'gradients': grads}] # recreating pl dict
+        values_list = cs_values + [values.pop(0) for i in range(len(values))] + pl_values + ps_values
+
     result = dict(zip(keys,values_list))
 
     # Cast values to original type