Add support for diabetes dataset

.github/workflows/python-ci.yml

@@ -26,12 +26,12 @@ jobs:
 
     steps:
       - name: Checkout code
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
         with:
           ref: ${{ github.event.pull_request.head.ref }} # ${{ github.event.pull_request.head.sha }} 
 
       - name: Setup Python 3.10
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v5
         with:
           python-version: "3.10"
           cache: 'pip'
@@ -70,13 +70,13 @@ jobs:
 
     steps:
     - name: Checkout to latest changes
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         ref: ${{ needs.formatting.outputs.new_sha }}
         fetch-depth: 0
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: "3.10"
         cache: 'pip'
@@ -94,13 +94,13 @@ jobs:
 
     steps:
     - name: Checkout to latest changes
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         ref: ${{ needs.formatting.outputs.new_sha }}
         fetch-depth: 0
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: "3.10"
         cache: 'pip'
@@ -125,13 +125,13 @@ jobs:
 
     steps:
     - name: Checkout to latest changes
-      uses: actions/checkout@v3
+      uses: actions/checkout@v4
       with:
         ref: ${{ needs.formatting.outputs.new_sha }}
         fetch-depth: 0
 
     - name: Set up Python 3.10
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: "3.10"
         cache: 'pip'

config.yaml

@@ -10,7 +10,7 @@
 ################################################################################
 
 ##############  Dataset type to use
-# Possible values: , kaggle_hf, mnist, dt4h_format
+# Possible values: , kaggle_hf, diabetes, mnist, dt4h_format
 dataset: dt4h_format
 #custom
 #libsvm
@@ -33,7 +33,7 @@ train_size: 0.7
 # ****** * * * * * * *  *  *   *    *    *  *  * * * * * * * *******************
 
 ##############  Number of clients (data centers) to use for training
-num_clients: 1
+num_clients: 3
 
 ############## Model type
 # Possible values: logistic_regression, lsvc, elastic_net, random_forest, weighted_random_forest, xgb
@@ -43,7 +43,7 @@ model: random_forest
 #random_forest
 
 ############## Training length
-num_rounds: 50
+num_rounds: 5
 
 ############## Metric to select the best model
 # Possible values: accuracy, balanced_accuracy, f1, precision, recall
@@ -87,6 +87,8 @@ smoothWeights:
 linear_models:
   n_features: 9
 
+n_features: 9
+
 # Random Forest
 random_forest:
   balanced_rf: true
@@ -101,7 +103,7 @@ xgb:
   batch_size: 32
   num_iterations: 100
   task_type: BINARY
-  tree_num: 500
+  tree_num: 10
 
 
 held_out_center_id: -1

flcore/client_selector.py

@@ -11,7 +11,7 @@ def get_model_client(config, data, client_id):
     if model in ("logistic_regression", "elastic_net", "lsvc"):
         client = linear_models.client.get_client(config,data,client_id)
 
-    elif model == "random_forest":
+    elif model in ("random_forest", "balanced_random_forest"):
         client = random_forest.client.get_client(config,data,client_id) 
 
     elif model == "weighted_random_forest":