diff --git a/edml/core/client.py b/edml/core/client.py
index ace69aca8f663b895b5dd8a84393e1bf8c563591..d8096c7ca5bc479a2aec458f2daf1cc442db4986 100644
--- a/edml/core/client.py
+++ b/edml/core/client.py
@@ -18,7 +18,7 @@ from edml.helpers.decorators import (
 from edml.helpers.flops import estimate_model_flops
 from edml.helpers.load_optimizer import get_optimizer_and_scheduler
 from edml.helpers.metrics import DiagnosticMetricResultContainer, DiagnosticMetricResult
-from edml.helpers.types import StateDict, SLTrainBatchResult
+from edml.helpers.types import StateDict
 
 if TYPE_CHECKING:
     from edml.core.device import Device
@@ -136,12 +136,18 @@ class DeviceClient:
 
     @check_device_set()
     def train_single_batch(
-        self, batch_index: int
+        self, batch_index: int, round_no: int = -1
     ) -> Optional[torch.Tensor, torch.Tensor]:
         torch.cuda.set_device(self._device)
         # We have to re-initialize the data loader in the case that we do another epoch.
         if batch_index == 0:
             self._batchable_data_loader = iter(self._train_data)
+            # update lr scheduler in the beginning of each round
+            if self._lr_scheduler is not None:
+                if round_no != -1:
+                    self._lr_scheduler.step(round_no)
+                else:
+                    self._lr_scheduler.step()
 
         # Used to measure training time. The problem we have with parallel split learning is that forward- and backward-
         # passes are orchestrated by the current server.
diff --git a/edml/core/device.py b/edml/core/device.py
index 1348bea7e4a4fadb44a6bd304bfc466f44bbba41..94bb18ac35241fc86eec778b8ef2d9145a8ed51f 100644
--- a/edml/core/device.py
+++ b/edml/core/device.py
@@ -207,7 +207,9 @@ class Device(ABC):
         """Evaluates a batch on the server of the device with the given id"""
 
     @abstractmethod
-    def train_batch_on_client_only_on(self, device_id: str, batch_index: int):
+    def train_batch_on_client_only_on(
+        self, device_id: str, batch_index: int, round_no: int
+    ):
         """"""
 
     @abstractmethod
@@ -265,17 +267,23 @@ class NetworkDevice(Device):
 
     @update_battery
     @log_execution_time("logger", "client_only_batch_train")
-    def train_batch_on_client_only(self, batch_index: int):
-        smashed_data, labels = self.client.train_single_batch(batch_index=batch_index)
+    def train_batch_on_client_only(self, batch_index: int, round_no: int):
+        smashed_data, labels = self.client.train_single_batch(
+            batch_index=batch_index, round_no=round_no
+        )
         return smashed_data, labels
 
     @update_battery
-    def train_batch_on_client_only_on(self, device_id: str, batch_index: int):
+    def train_batch_on_client_only_on(
+        self, device_id: str, batch_index: int, round_no: int
+    ):
         if self.device_id == device_id:
-            return self.train_batch_on_client_only(batch_index=batch_index)
+            return self.train_batch_on_client_only(
+                batch_index=batch_index, round_no=round_no
+            )
         else:
             return self.request_dispatcher.train_batch_on_client_only(
-                device_id=device_id, batch_index=batch_index
+                device_id=device_id, batch_index=batch_index, round_no=round_no
             )
 
     def __init__(
@@ -575,8 +583,11 @@ class RPCDeviceServicer(DeviceServicer):
 
     def TrainSingleBatchOnClient(self, request, context):
         batch_index = request.batch_index
+        round_no = request.round_no
 
-        smashed_data, labels = self.device.client.train_single_batch(batch_index)
+        smashed_data, labels = self.device.client.train_single_batch(
+            batch_index, round_no=round_no
+        )
 
         smashed_data = Activations(activations=tensor_to_proto(smashed_data))
         labels = Labels(labels=tensor_to_proto(labels))
@@ -955,13 +966,15 @@ class DeviceRequestDispatcher:
         return False
 
     def train_batch_on_client_only(
-        self, device_id: str, batch_index: int
+        self, device_id: str, batch_index: int, round_no: int
     ) -> Tuple[Tensor, Tensor] | None:
         try:
             response: SingleBatchTrainingResponse = self._get_connection(
                 device_id
             ).TrainSingleBatchOnClient(
-                connection_pb2.SingleBatchTrainingRequest(batch_index=batch_index)
+                connection_pb2.SingleBatchTrainingRequest(
+                    batch_index=batch_index, round_no=round_no
+                )
             )
 
             # The response can only be None if the last batch was smaller than the configured batch size.
diff --git a/edml/core/server.py b/edml/core/server.py
index c0e60fb928ddbdaa2022ff08c5fb6192aa36cf10..7503389f0c080b2eddb33bf9b932bbbdcf76d758 100644
--- a/edml/core/server.py
+++ b/edml/core/server.py
@@ -220,7 +220,10 @@ class DeviceServer:
     ):
         def client_training_job(client_id: str, batch_index: int):
             result = self.node_device.train_batch_on_client_only_on(
-                device_id=client_id, batch_index=batch_index
+                device_id=client_id,
+                batch_index=batch_index,
+                round_no=round_no,
+                # round_no is taken from outer method arg
             )
             return (client_id, result)
 
diff --git a/edml/generated/connection_pb2.py b/edml/generated/connection_pb2.py
index ce271bbab4fa5d30e0e8c5a0a8b309d96eaccf07..f3c72442f44b99d587925a030002e1af92924ac8 100644
--- a/edml/generated/connection_pb2.py
+++ b/edml/generated/connection_pb2.py
@@ -14,7 +14,7 @@ _sym_db = _symbol_database.Default()
 import datastructures_pb2 as datastructures__pb2
 
 
-DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x63onnection.proto\x1a\x14\x64\x61tastructures.proto\"4\n\x13SetGradientsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"5\n\x14UpdateWeightsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\";\n\x1aSingleBatchBackwardRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"j\n\x1bSingleBatchBackwardResponse\x12\x19\n\x07metrics\x18\x01 \x01(\x0b\x32\x08.Metrics\x12\"\n\tgradients\x18\x02 \x01(\x0b\x32\n.GradientsH\x00\x88\x01\x01\x42\x0c\n\n_gradients\"1\n\x1aSingleBatchTrainingRequest\x12\x13\n\x0b\x62\x61tch_index\x18\x01 \x01(\x05\"\x80\x01\n\x1bSingleBatchTrainingResponse\x12\'\n\x0csmashed_data\x18\x01 \x01(\x0b\x32\x0c.ActivationsH\x00\x88\x01\x01\x12\x1c\n\x06labels\x18\x02 \x01(\x0b\x32\x07.LabelsH\x01\x88\x01\x01\x42\x0f\n\r_smashed_dataB\t\n\x07_labels\"\xd5\x01\n\'TrainGlobalParallelSplitLearningRequest\x12\x15\n\x08round_no\x18\x01 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+DESCRIPTOR = _descriptor_pool.Default().AddSerializedFile(b'\n\x10\x63onnection.proto\x1a\x14\x64\x61tastructures.proto\"4\n\x13SetGradientsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"5\n\x14UpdateWeightsRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\";\n\x1aSingleBatchBackwardRequest\x12\x1d\n\tgradients\x18\x01 \x01(\x0b\x32\n.Gradients\"j\n\x1bSingleBatchBackwardResponse\x12\x19\n\x07metrics\x18\x01 \x01(\x0b\x32\x08.Metrics\x12\"\n\tgradients\x18\x02 \x01(\x0b\x32\n.GradientsH\x00\x88\x01\x01\x42\x0c\n\n_gradients\"C\n\x1aSingleBatchTrainingRequest\x12\x13\n\x0b\x62\x61tch_index\x18\x01 \x01(\x05\x12\x10\n\x08round_no\x18\x02 \x01(\x05\"\x80\x01\n\x1bSingleBatchTrainingResponse\x12\'\n\x0csmashed_data\x18\x01 \x01(\x0b\x32\x0c.ActivationsH\x00\x88\x01\x01\x12\x1c\n\x06labels\x18\x02 \x01(\x0b\x32\x07.LabelsH\x01\x88\x01\x01\x42\x0f\n\r_smashed_dataB\t\n\x07_labels\"\xd5\x01\n\'TrainGlobalParallelSplitLearningRequest\x12\x15\n\x08round_no\x18\x01 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\x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\">\n\x0b\x45valRequest\x12\x1b\n\x06server\x18\x01 \x01(\x0b\x32\x0b.DeviceInfo\x12\x12\n\nvalidation\x18\x02 \x01(\x08\"P\n\x0c\x45valResponse\x12)\n\x12\x64iagnostic_metrics\x18\x01 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\"O\n\x10\x45valBatchRequest\x12\"\n\x0csmashed_data\x18\x01 \x01(\x0b\x32\x0c.Activations\x12\x17\n\x06labels\x18\x02 \x01(\x0b\x32\x07.Labels\"p\n\x11\x45valBatchResponse\x12\x19\n\x07metrics\x18\x01 \x01(\x0b\x32\x08.Metrics\x12)\n\x12\x64iagnostic_metrics\x18\x02 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\";\n\x15\x46ullModelTrainRequest\x12\x15\n\x08round_no\x18\x01 \x01(\x05H\x00\x88\x01\x01\x42\x0b\n\t_round_no\"\xce\x01\n\x16\x46ullModelTrainResponse\x12 \n\x0e\x63lient_weights\x18\x01 \x01(\x0b\x32\x08.Weights\x12 \n\x0eserver_weights\x18\x02 \x01(\x0b\x32\x08.Weights\x12\x13\n\x0bnum_samples\x18\x03 \x01(\x05\x12\x19\n\x07metrics\x18\x04 \x01(\x0b\x32\x08.Metrics\x12)\n\x12\x64iagnostic_metrics\x18\x05 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\"\x18\n\x16StartExperimentRequest\"[\n\x17StartExperimentResponse\x12)\n\x12\x64iagnostic_metrics\x18\x01 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\"\x16\n\x14\x45ndExperimentRequest\"Y\n\x15\x45ndExperimentResponse\x12)\n\x12\x64iagnostic_metrics\x18\x01 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\"\x16\n\x14\x42\x61tteryStatusRequest\"y\n\x15\x42\x61tteryStatusResponse\x12\x1e\n\x06status\x18\x01 \x01(\x0b\x32\x0e.BatteryStatus\x12)\n\x12\x64iagnostic_metrics\x18\x02 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics\"\x19\n\x17\x44\x61tasetModelInfoRequest\"\xc7\x01\n\x18\x44\x61tasetModelInfoResponse\x12\x15\n\rtrain_samples\x18\x01 \x01(\x05\x12\x1a\n\x12validation_samples\x18\x02 \x01(\x05\x12\x1a\n\x12\x63lient_model_flops\x18\x03 \x01(\x05\x12\x1a\n\x12server_model_flops\x18\x04 \x01(\x05\x12)\n\x12\x64iagnostic_metrics\x18\x05 \x01(\x0b\x32\x08.MetricsH\x00\x88\x01\x01\x42\x15\n\x13_diagnostic_metrics2\xf8\x08\n\x06\x44\x65vice\x12:\n\x0bTrainGlobal\x12\x13.TrainGlobalRequest\x1a\x14.TrainGlobalResponse\"\x00\x12\x37\n\nSetWeights\x12\x12.SetWeightsRequest\x1a\x13.SetWeightsResponse\"\x00\x12\x37\n\nTrainEpoch\x12\x12.TrainEpochRequest\x1a\x13.TrainEpochResponse\"\x00\x12\x37\n\nTrainBatch\x12\x12.TrainBatchRequest\x1a\x13.TrainBatchResponse\"\x00\x12;\n\x0e\x45valuateGlobal\x12\x12.EvalGlobalRequest\x1a\x13.EvalGlobalResponse\"\x00\x12)\n\x08\x45valuate\x12\x0c.EvalRequest\x1a\r.EvalResponse\"\x00\x12\x38\n\rEvaluateBatch\x12\x11.EvalBatchRequest\x1a\x12.EvalBatchResponse\"\x00\x12\x46\n\x11\x46ullModelTraining\x12\x16.FullModelTrainRequest\x1a\x17.FullModelTrainResponse\"\x00\x12\x46\n\x0fStartExperiment\x12\x17.StartExperimentRequest\x1a\x18.StartExperimentResponse\"\x00\x12@\n\rEndExperiment\x12\x15.EndExperimentRequest\x1a\x16.EndExperimentResponse\"\x00\x12\x43\n\x10GetBatteryStatus\x12\x15.BatteryStatusRequest\x1a\x16.BatteryStatusResponse\"\x00\x12L\n\x13GetDatasetModelInfo\x12\x18.DatasetModelInfoRequest\x1a\x19.DatasetModelInfoResponse\"\x00\x12y\n TrainGlobalParallelSplitLearning\x12(.TrainGlobalParallelSplitLearningRequest\x1a).TrainGlobalParallelSplitLearningResponse\"\x00\x12W\n\x18TrainSingleBatchOnClient\x12\x1b.SingleBatchTrainingRequest\x1a\x1c.SingleBatchTrainingResponse\"\x00\x12\x65\n&BackwardPropagationSingleBatchOnClient\x12\x1b.SingleBatchBackwardRequest\x1a\x1c.SingleBatchBackwardResponse\"\x00\x12\x45\n#SetGradientsAndFinalizeTrainingStep\x12\x14.SetGradientsRequest\x1a\x06.Empty\"\x00\x62\x06proto3')
 
 _globals = globals()
 _builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
@@ -31,61 +31,61 @@ if _descriptor._USE_C_DESCRIPTORS == False:
   _globals['_SINGLEBATCHBACKWARDRESPONSE']._serialized_start=212
   _globals['_SINGLEBATCHBACKWARDRESPONSE']._serialized_end=318
   _globals['_SINGLEBATCHTRAININGREQUEST']._serialized_start=320
-  _globals['_SINGLEBATCHTRAININGREQUEST']._serialized_end=369
-  _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_start=372
-  _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_end=500
-  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_start=503
-  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_end=716
-  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_start=719
-  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_end=984
-  _globals['_TRAINGLOBALREQUEST']._serialized_start=987
-  _globals['_TRAINGLOBALREQUEST']._serialized_end=1121
-  _globals['_TRAINGLOBALRESPONSE']._serialized_start=1124
-  _globals['_TRAINGLOBALRESPONSE']._serialized_end=1368
-  _globals['_SETWEIGHTSREQUEST']._serialized_start=1370
-  _globals['_SETWEIGHTSREQUEST']._serialized_end=1435
-  _globals['_SETWEIGHTSRESPONSE']._serialized_start=1437
-  _globals['_SETWEIGHTSRESPONSE']._serialized_end=1523
-  _globals['_TRAINEPOCHREQUEST']._serialized_start=1525
-  _globals['_TRAINEPOCHREQUEST']._serialized_end=1609
-  _globals['_TRAINEPOCHRESPONSE']._serialized_start=1611
-  _globals['_TRAINEPOCHRESPONSE']._serialized_end=1724
-  _globals['_TRAINBATCHREQUEST']._serialized_start=1726
-  _globals['_TRAINBATCHREQUEST']._serialized_end=1806
-  _globals['_TRAINBATCHRESPONSE']._serialized_start=1809
-  _globals['_TRAINBATCHRESPONSE']._serialized_end=1954
-  _globals['_EVALGLOBALREQUEST']._serialized_start=1956
-  _globals['_EVALGLOBALREQUEST']._serialized_end=2014
-  _globals['_EVALGLOBALRESPONSE']._serialized_start=2016
-  _globals['_EVALGLOBALRESPONSE']._serialized_end=2129
-  _globals['_EVALREQUEST']._serialized_start=2131
-  _globals['_EVALREQUEST']._serialized_end=2193
-  _globals['_EVALRESPONSE']._serialized_start=2195
-  _globals['_EVALRESPONSE']._serialized_end=2275
-  _globals['_EVALBATCHREQUEST']._serialized_start=2277
-  _globals['_EVALBATCHREQUEST']._serialized_end=2356
-  _globals['_EVALBATCHRESPONSE']._serialized_start=2358
-  _globals['_EVALBATCHRESPONSE']._serialized_end=2470
-  _globals['_FULLMODELTRAINREQUEST']._serialized_start=2472
-  _globals['_FULLMODELTRAINREQUEST']._serialized_end=2531
-  _globals['_FULLMODELTRAINRESPONSE']._serialized_start=2534
-  _globals['_FULLMODELTRAINRESPONSE']._serialized_end=2740
-  _globals['_STARTEXPERIMENTREQUEST']._serialized_start=2742
-  _globals['_STARTEXPERIMENTREQUEST']._serialized_end=2766
-  _globals['_STARTEXPERIMENTRESPONSE']._serialized_start=2768
-  _globals['_STARTEXPERIMENTRESPONSE']._serialized_end=2859
-  _globals['_ENDEXPERIMENTREQUEST']._serialized_start=2861
-  _globals['_ENDEXPERIMENTREQUEST']._serialized_end=2883
-  _globals['_ENDEXPERIMENTRESPONSE']._serialized_start=2885
-  _globals['_ENDEXPERIMENTRESPONSE']._serialized_end=2974
-  _globals['_BATTERYSTATUSREQUEST']._serialized_start=2976
-  _globals['_BATTERYSTATUSREQUEST']._serialized_end=2998
-  _globals['_BATTERYSTATUSRESPONSE']._serialized_start=3000
-  _globals['_BATTERYSTATUSRESPONSE']._serialized_end=3121
-  _globals['_DATASETMODELINFOREQUEST']._serialized_start=3123
-  _globals['_DATASETMODELINFOREQUEST']._serialized_end=3148
-  _globals['_DATASETMODELINFORESPONSE']._serialized_start=3151
-  _globals['_DATASETMODELINFORESPONSE']._serialized_end=3350
-  _globals['_DEVICE']._serialized_start=3353
-  _globals['_DEVICE']._serialized_end=4497
+  _globals['_SINGLEBATCHTRAININGREQUEST']._serialized_end=387
+  _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_start=390
+  _globals['_SINGLEBATCHTRAININGRESPONSE']._serialized_end=518
+  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_start=521
+  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGREQUEST']._serialized_end=734
+  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_start=737
+  _globals['_TRAINGLOBALPARALLELSPLITLEARNINGRESPONSE']._serialized_end=1002
+  _globals['_TRAINGLOBALREQUEST']._serialized_start=1005
+  _globals['_TRAINGLOBALREQUEST']._serialized_end=1139
+  _globals['_TRAINGLOBALRESPONSE']._serialized_start=1142
+  _globals['_TRAINGLOBALRESPONSE']._serialized_end=1386
+  _globals['_SETWEIGHTSREQUEST']._serialized_start=1388
+  _globals['_SETWEIGHTSREQUEST']._serialized_end=1453
+  _globals['_SETWEIGHTSRESPONSE']._serialized_start=1455
+  _globals['_SETWEIGHTSRESPONSE']._serialized_end=1541
+  _globals['_TRAINEPOCHREQUEST']._serialized_start=1543
+  _globals['_TRAINEPOCHREQUEST']._serialized_end=1627
+  _globals['_TRAINEPOCHRESPONSE']._serialized_start=1629
+  _globals['_TRAINEPOCHRESPONSE']._serialized_end=1742
+  _globals['_TRAINBATCHREQUEST']._serialized_start=1744
+  _globals['_TRAINBATCHREQUEST']._serialized_end=1824
+  _globals['_TRAINBATCHRESPONSE']._serialized_start=1827
+  _globals['_TRAINBATCHRESPONSE']._serialized_end=1972
+  _globals['_EVALGLOBALREQUEST']._serialized_start=1974
+  _globals['_EVALGLOBALREQUEST']._serialized_end=2032
+  _globals['_EVALGLOBALRESPONSE']._serialized_start=2034
+  _globals['_EVALGLOBALRESPONSE']._serialized_end=2147
+  _globals['_EVALREQUEST']._serialized_start=2149
+  _globals['_EVALREQUEST']._serialized_end=2211
+  _globals['_EVALRESPONSE']._serialized_start=2213
+  _globals['_EVALRESPONSE']._serialized_end=2293
+  _globals['_EVALBATCHREQUEST']._serialized_start=2295
+  _globals['_EVALBATCHREQUEST']._serialized_end=2374
+  _globals['_EVALBATCHRESPONSE']._serialized_start=2376
+  _globals['_EVALBATCHRESPONSE']._serialized_end=2488
+  _globals['_FULLMODELTRAINREQUEST']._serialized_start=2490
+  _globals['_FULLMODELTRAINREQUEST']._serialized_end=2549
+  _globals['_FULLMODELTRAINRESPONSE']._serialized_start=2552
+  _globals['_FULLMODELTRAINRESPONSE']._serialized_end=2758
+  _globals['_STARTEXPERIMENTREQUEST']._serialized_start=2760
+  _globals['_STARTEXPERIMENTREQUEST']._serialized_end=2784
+  _globals['_STARTEXPERIMENTRESPONSE']._serialized_start=2786
+  _globals['_STARTEXPERIMENTRESPONSE']._serialized_end=2877
+  _globals['_ENDEXPERIMENTREQUEST']._serialized_start=2879
+  _globals['_ENDEXPERIMENTREQUEST']._serialized_end=2901
+  _globals['_ENDEXPERIMENTRESPONSE']._serialized_start=2903
+  _globals['_ENDEXPERIMENTRESPONSE']._serialized_end=2992
+  _globals['_BATTERYSTATUSREQUEST']._serialized_start=2994
+  _globals['_BATTERYSTATUSREQUEST']._serialized_end=3016
+  _globals['_BATTERYSTATUSRESPONSE']._serialized_start=3018
+  _globals['_BATTERYSTATUSRESPONSE']._serialized_end=3139
+  _globals['_DATASETMODELINFOREQUEST']._serialized_start=3141
+  _globals['_DATASETMODELINFOREQUEST']._serialized_end=3166
+  _globals['_DATASETMODELINFORESPONSE']._serialized_start=3169
+  _globals['_DATASETMODELINFORESPONSE']._serialized_end=3368
+  _globals['_DEVICE']._serialized_start=3371
+  _globals['_DEVICE']._serialized_end=4515
 # @@protoc_insertion_point(module_scope)
diff --git a/edml/generated/connection_pb2.pyi b/edml/generated/connection_pb2.pyi
index a9735505e808ee35b156158e972ab6b206e90b0a..89353343aa1c7e39072bd0ea03c891bd2df7b4df 100644
--- a/edml/generated/connection_pb2.pyi
+++ b/edml/generated/connection_pb2.pyi
@@ -32,10 +32,12 @@ class SingleBatchBackwardResponse(_message.Message):
     def __init__(self, metrics: _Optional[_Union[_datastructures_pb2.Metrics, _Mapping]] = ..., gradients: _Optional[_Union[_datastructures_pb2.Gradients, _Mapping]] = ...) -> None: ...
 
 class SingleBatchTrainingRequest(_message.Message):
-    __slots__ = ["batch_index"]
+    __slots__ = ["batch_index", "round_no"]
     BATCH_INDEX_FIELD_NUMBER: _ClassVar[int]
+    ROUND_NO_FIELD_NUMBER: _ClassVar[int]
     batch_index: int
-    def __init__(self, batch_index: _Optional[int] = ...) -> None: ...
+    round_no: int
+    def __init__(self, batch_index: _Optional[int] = ..., round_no: _Optional[int] = ...) -> None: ...
 
 class SingleBatchTrainingResponse(_message.Message):
     __slots__ = ["smashed_data", "labels"]
diff --git a/edml/proto/connection.proto b/edml/proto/connection.proto
index 2f12881a1cf7e3ee6ae2e076b2f9005141cfece2..5755518d01796bdff68957655d4d339dcf02ab1d 100644
--- a/edml/proto/connection.proto
+++ b/edml/proto/connection.proto
@@ -41,6 +41,7 @@ message SingleBatchBackwardResponse {
 
 message SingleBatchTrainingRequest {
   int32 batch_index = 1;
+  int32 round_no = 2;
 }
 
 message SingleBatchTrainingResponse {