modify dpsgd_strategy optimizer

paddle_fl/core/strategy/fl_strategy_base.py

@@ -110,10 +110,44 @@ class DPSGDStrategy(FLStrategyBase):
     def __init__(self):
         super(DPSGDStrategy, self).__init__()
 
+    @property
+    def learning_rate(self):
+        return self._learning_rate
+
+    @learning_rate.setter
+    def learning_rate(self, s):
+        self._learning_rate = s
+
+    @property
+    def clip(self):
+        return self._clip
+
+    @clip.setter
+    def clip(self, s):
+        self._clip = s
+
+    @property
+    def batch_size(self):
+        return self._batch_size
+
+    @batch_size.setter
+    def batch_size(self, s):
+        self._batch_size = s
+
+    @property
+    def sigma(self):
+        return self._sigma
+
+    @sigma.setter
+    def sigma(self, s):
+        self._sigma = s
+
     def minimize(self, optimizer=None, losses=[]):
         """
-        Do nothing in DPSGDStrategy in minimize function
+        Define Dpsgd optimizeer
         """
+        optimizer = fluid.optimizer.Dpsgd(self._learning_rate, clip=self._clip, batch_size=self._batch_size, sigma=self._sigma)
+        print(str(optimizer))
         optimizer.minimize(losses[0])
 
     def _build_trainer_program_for_job(

paddle_fl/examples/dpsgd_demo/fl_master.py

@@ -28,8 +28,7 @@ CLIP = 4.0
 batch_size = 64
 
 job_generator = JobGenerator()
-optimizer = fluid.optimizer.Dpsgd(0.1, clip=CLIP, batch_size=float(batch_size), sigma=CLIP * SIGMA)
-# optimizer = fluid.optimizer.SGD(learning_rate=0.1)
+optimizer = fluid.optimizer.SGD(learning_rate=0.1)
 job_generator.set_optimizer(optimizer)
 job_generator.set_losses([model.loss])
 job_generator.set_startup_program(model.startup_program)
@@ -40,6 +39,10 @@ build_strategy = FLStrategyFactory()
 build_strategy.dpsgd = True
 build_strategy.inner_step = 1
 strategy = build_strategy.create_fl_strategy()
+strategy.learning_rate = 0.1
+strategy.clip = CLIP
+strategy.batch_size = float(batch_size)
+strategy.sigma = CLIP * SIGMA
 
 # endpoints will be collected through the cluster
 # in this example, we suppose endpoints have been collected