Version 2.0

MedicalDiagnosis/Main_AFedSV.py → MedicalDiagnosis/AFedSV.py

@@ -1,6 +1,3 @@
-import sys
-sys.path.insert(0,"/home/lixiang/FLamby-main")
-
 import torch
 
 from MedicalDiagnosis.utils import evaluate_model_on_tests
@@ -19,7 +16,7 @@
 from MedicalDiagnosis.datasets.fed_isic2019 import FedIsic2019 as FedDataset
 
 # 1st line of code to change to switch to another strategy
-from MedicalDiagnosis.strategies.fed_lsv import FedLSV as strat
+from MedicalDiagnosis.strategies.afedsv import FedOptSV as strat
 
 # We loop on all the clients of the distributed dataset and instantiate associated data loaders
 train_dataloaders = [

MedicalDiagnosis/FedAvg.py

@@ -0,0 +1,77 @@
+import torch
+from MedicalDiagnosis.utils import evaluate_model_on_tests
+
+print(torch.Tensor([1,2]).cuda())
+# 2 lines of code to change to switch to another dataset
+from MedicalDiagnosis.datasets.fed_isic2019 import (
+    BATCH_SIZE,
+    LR,
+    NUM_EPOCHS_POOLED,
+    Baseline,
+    BaselineLoss,
+    metric,
+    NUM_CLIENTS,
+    get_nb_max_rounds
+)
+from MedicalDiagnosis.datasets.fed_isic2019 import FedIsic2019 as FedDataset
+
+# 1st line of code to change to switch to another strategy
+from MedicalDiagnosis.strategies.fed_avg import FedAvg as strat
+
+# We loop on all the clients of the distributed dataset and instantiate associated data loaders
+train_dataloaders = [
+            torch.utils.data.DataLoader(
+                FedDataset(center = i, train = True, pooled = False),
+                batch_size = BATCH_SIZE,
+                shuffle = True,
+                num_workers = 0
+            )
+            for i in range(NUM_CLIENTS)
+        ]
+full_dataset = FedDataset(train = False, pooled = True)
+valid_size = int(0.25 * len(full_dataset))
+test_size = len(full_dataset)  -valid_size
+valid_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [valid_size, test_size])
+print(len(valid_dataset), len(test_dataset))
+test_dataloaders = [
+            torch.utils.data.DataLoader(
+                test_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+valid_dataloaders = [
+            torch.utils.data.DataLoader(
+                valid_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+lossfunc = BaselineLoss()
+m = Baseline()
+
+# Federated Learning loop
+# 2nd line of code to change to switch to another strategy (feed the FL strategy the right HPs)
+args = {
+            "training_dataloaders": train_dataloaders,
+            "valid_dataloaders": valid_dataloaders,
+            "test_dataloaders": test_dataloaders,
+            "model": m,
+            "loss": lossfunc,
+            "optimizer_class": torch.optim.SGD,
+            "learning_rate": 0.01,
+            "num_updates": 100,
+# This helper function returns the number of rounds necessary to perform approximately as many
+# epochs on each local dataset as with the pooled training
+            "nrounds": 25,
+        }
+s = strat(**args)
+seeds = [20,21,22,23,24]
+for seed in seeds:
+    m = s.run(seed)[0]
+
+# Evaluation
+# We only instantiate one test set in this particular case: the pooled one
+

MedicalDiagnosis/FedSV.py

@@ -0,0 +1,77 @@
+import torch
+from MedicalDiagnosis.utils import evaluate_model_on_tests
+
+print(torch.Tensor([1,2]).cuda())
+# 2 lines of code to change to switch to another dataset
+from MedicalDiagnosis.datasets.fed_isic2019 import (
+    BATCH_SIZE,
+    LR,
+    NUM_EPOCHS_POOLED,
+    Baseline,
+    BaselineLoss,
+    metric,
+    NUM_CLIENTS,
+    get_nb_max_rounds
+)
+from MedicalDiagnosis.datasets.fed_isic2019 import FedIsic2019 as FedDataset
+
+# 1st line of code to change to switch to another strategy
+from MedicalDiagnosis.strategies.fed_lsv import FedLSV as strat
+
+# We loop on all the clients of the distributed dataset and instantiate associated data loaders
+train_dataloaders = [
+            torch.utils.data.DataLoader(
+                FedDataset(center = i, train = True, pooled = False),
+                batch_size = BATCH_SIZE,
+                shuffle = True,
+                num_workers = 0
+            )
+            for i in range(NUM_CLIENTS)
+        ]
+full_dataset = FedDataset(train = False, pooled = True)
+valid_size = int(0.25 * len(full_dataset))
+test_size = len(full_dataset)  -valid_size
+valid_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [valid_size, test_size])
+print(len(valid_dataset), len(test_dataset))
+test_dataloaders = [
+            torch.utils.data.DataLoader(
+                test_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+valid_dataloaders = [
+            torch.utils.data.DataLoader(
+                valid_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+lossfunc = BaselineLoss()
+m = Baseline()
+
+# Federated Learning loop
+# 2nd line of code to change to switch to another strategy (feed the FL strategy the right HPs)
+args = {
+            "training_dataloaders": train_dataloaders,
+            "valid_dataloaders": valid_dataloaders,
+            "test_dataloaders": test_dataloaders,
+            "model": m,
+            "loss": lossfunc,
+            "optimizer_class": torch.optim.SGD,
+            "learning_rate": 0.01,
+            "num_updates": 100,
+# This helper function returns the number of rounds necessary to perform approximately as many
+# epochs on each local dataset as with the pooled training
+            "nrounds": 25,
+        }
+s = strat(**args)
+seeds = [20,21,22,23,24]
+for seed in seeds:
+    m = s.run(seed)[0]
+
+# Evaluation
+# We only instantiate one test set in this particular case: the pooled one
+

MedicalDiagnosis/RFA.py

@@ -0,0 +1,77 @@
+import torch
+from MedicalDiagnosis.utils import evaluate_model_on_tests
+
+print(torch.Tensor([1,2]).cuda())
+# 2 lines of code to change to switch to another dataset
+from MedicalDiagnosis.datasets.fed_isic2019 import (
+    BATCH_SIZE,
+    LR,
+    NUM_EPOCHS_POOLED,
+    Baseline,
+    BaselineLoss,
+    metric,
+    NUM_CLIENTS,
+    get_nb_max_rounds
+)
+from MedicalDiagnosis.datasets.fed_isic2019 import FedIsic2019 as FedDataset
+
+# 1st line of code to change to switch to another strategy
+from MedicalDiagnosis.strategies.rfa import RFA as strat
+
+# We loop on all the clients of the distributed dataset and instantiate associated data loaders
+train_dataloaders = [
+            torch.utils.data.DataLoader(
+                FedDataset(center = i, train = True, pooled = False),
+                batch_size = BATCH_SIZE,
+                shuffle = True,
+                num_workers = 0
+            )
+            for i in range(NUM_CLIENTS)
+        ]
+full_dataset = FedDataset(train = False, pooled = True)
+valid_size = int(0.25 * len(full_dataset))
+test_size = len(full_dataset)  -valid_size
+valid_dataset, test_dataset = torch.utils.data.random_split(full_dataset, [valid_size, test_size])
+print(len(valid_dataset), len(test_dataset))
+test_dataloaders = [
+            torch.utils.data.DataLoader(
+                test_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+valid_dataloaders = [
+            torch.utils.data.DataLoader(
+                valid_dataset,
+                batch_size = BATCH_SIZE,
+                shuffle = False,
+                num_workers = 0,
+            )
+        ]
+lossfunc = BaselineLoss()
+m = Baseline()
+
+# Federated Learning loop
+# 2nd line of code to change to switch to another strategy (feed the FL strategy the right HPs)
+args = {
+            "training_dataloaders": train_dataloaders,
+            "valid_dataloaders": valid_dataloaders,
+            "test_dataloaders": test_dataloaders,
+            "model": m,
+            "loss": lossfunc,
+            "optimizer_class": torch.optim.SGD,
+            "learning_rate": 0.01,
+            "num_updates": 100,
+# This helper function returns the number of rounds necessary to perform approximately as many
+# epochs on each local dataset as with the pooled training
+            "nrounds": 25,
+        }
+s = strat(**args)
+seeds = [20,21,22,23,24]
+for seed in seeds:
+    m = s.run(seed)[0]
+
+# Evaluation
+# We only instantiate one test set in this particular case: the pooled one
+