Revert "Add gradient policy entry in config"

This reverts commit ce81052.
SymbioticLab · Sep 5, 2023 · 5aa9492 · 5aa9492
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commit 5aa9492
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Showing 16 changed files with 6 additions and 22 deletions.
diff --git a/benchmark/configs/android/mnn.yml b/benchmark/configs/android/mnn.yml
@@ -25,7 +25,6 @@ job_conf:
     - experiment_mode: mobile
     - num_participants: 1                # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - model: linear                      # Need to define the model in aggregator_mnn.py
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - learning_rate: 0.01
     - batch_size: 32
     - input_shape: 32 32 3

diff --git a/benchmark/configs/android/tflite.yml b/benchmark/configs/android/tflite.yml
@@ -25,7 +25,6 @@ job_conf:
     - experiment_mode: mobile
     - num_participants: 1                # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - model: linear                      # Need to define the model in tf_aggregator.py
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - learning_rate: 0.01
     - batch_size: 32
     - input_shape: 32 32 3

diff --git a/benchmark/configs/cifar_cpu/cifar_cpu.yml b/benchmark/configs/cifar_cpu/cifar_cpu.yml
@@ -36,7 +36,6 @@ job_conf:
     - data_dir: $FEDSCALE_HOME/benchmark/dataset/data/    # Path of the dataset
     - model: shufflenet_v2_x2_0              # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo              # Default zoo (torchcv) uses the pytorchvision zoo, which can not support small images well
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 5                     # How many rounds to run a testing on the testing set
     - rounds: 600                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/docker_deploy/cifar_cpu_docker.yml b/benchmark/configs/docker_deploy/cifar_cpu_docker.yml
@@ -55,7 +55,6 @@ job_conf:
     - data_dir: /FedScale/benchmark/dataset/data/    # Path of the dataset
     - model: shufflenet_v2_x2_0              # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo              # Default zoo (torchcv) uses the pytorchvision zoo, which can not support small images well
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 21                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/docker_deploy/dry_run_docker.yml b/benchmark/configs/docker_deploy/dry_run_docker.yml
@@ -54,8 +54,7 @@ job_conf:
     - num_participants: 4                      # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - data_set: cifar10                     # Dataset: openImg, google_speech, stackoverflow
     - data_dir: /FedScale/benchmark/dataset/data/    # Path of the dataset
-    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
+    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2# - gradient_policy: yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 20                       # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/docker_deploy/femnist_docker.yml b/benchmark/configs/docker_deploy/femnist_docker.yml
@@ -59,7 +59,6 @@ job_conf:
     - device_avail_file: /FedScale/benchmark/dataset/data/device_info/client_behave_trace
     - model: resnet18             # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 20                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 21                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/dry_run/dry_run.yml b/benchmark/configs/dry_run/dry_run.yml
@@ -35,8 +35,7 @@ job_conf:
     - num_participants: 4                      # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - data_set: cifar10                     # Dataset: openImg, google_speech, stackoverflow
     - data_dir: $FEDSCALE_HOME/benchmark/dataset/data/    # Path of the dataset
-    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
+    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2# - gradient_policy: yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 5                     # How many rounds to run a testing on the testing set
     - rounds: 200                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/fedbuff_femnist/conf.yml b/benchmark/configs/fedbuff_femnist/conf.yml
@@ -39,7 +39,6 @@ job_conf:
     - device_avail_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_behave_trace
     - model: resnet18                       # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 1000                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 21                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/femnist/conf.yml b/benchmark/configs/femnist/conf.yml
@@ -39,9 +39,8 @@ job_conf:
     - device_avail_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_behave_trace
     - model: resnet18             # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo
-    - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
-    - eval_interval: 20                     # How many rounds to run a testing on the testing set
-    - rounds: 20                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
+    - eval_interval: 10                     # How many rounds to run a testing on the testing set
+    - rounds: 1000                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 21                       # Remove clients w/ less than 21 samples
     - num_loaders: 2
     - local_steps: 5

diff --git a/benchmark/configs/k8s_deploy/cifar_cpu_k8s.yml b/benchmark/configs/k8s_deploy/cifar_cpu_k8s.yml
@@ -37,7 +37,6 @@ job_conf:
     - data_dir: /FedScale/benchmark/dataset/data/    # Path of the dataset
     - model: shufflenet_v2_x2_0              # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo              # Default zoo (torchcv) uses the pytorchvision zoo, which can not support small images well
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 21                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/k8s_deploy/dry_run_k8s.yml b/benchmark/configs/k8s_deploy/dry_run_k8s.yml
@@ -35,8 +35,7 @@ job_conf:
     - num_participants: 4                      # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - data_set: cifar10                     # Dataset: openImg, google_speech, stackoverflow
     - data_dir: /FedScale/benchmark/dataset/data/    # Path of the dataset
-    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
+    - model: resnet18                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2# - gradient_policy: yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 21                       # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/k8s_deploy/femnist_k8s.yml b/benchmark/configs/k8s_deploy/femnist_k8s.yml
@@ -41,7 +41,6 @@ job_conf:
     - device_avail_file: /FedScale/benchmark/dataset/data/device_info/client_behave_trace
     - model: resnet18             # NOTE: Please refer to our model zoo README and use models for these small image (e.g., 32x32x3) inputs
 #    - model_zoo: fedscale-torch-zoo
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 10                     # How many rounds to run a testing on the testing set
     - rounds: 21                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 21                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/others/heterofl.yml b/benchmark/configs/others/heterofl.yml
@@ -36,8 +36,7 @@ job_conf:
     - num_participants: 10                      # Number of participants per round, we use K=100 in our paper, large K will be much slower
     - data_set: cifar10                     # Dataset: openImg, google_speech, stackoverflow
     - data_dir: $FEDSCALE_HOME/benchmark/dataset/data/    # Path of the dataset
-    - model: resnet_heterofl                      # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
+    - model: resnet_heterofl                      # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2# - gradient_policy: yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 5                     # How many rounds to run a testing on the testing set
     - rounds: 400                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/reddit/reddit.yml b/benchmark/configs/reddit/reddit.yml
@@ -50,7 +50,6 @@ job_conf:
     - device_conf_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_device_capacity     # Path of the client trace
     - device_avail_file: $FEDSCALE_HOME/benchmark/dataset/data/device_info/client_behave_trace
     - model: albert-base-v2                            # Models: e.g., shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 30                     # How many rounds to run a testing on the testing set
     - rounds: 5000                          # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 21                       # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/tf_cifar/tf_cifar.yml b/benchmark/configs/tf_cifar/tf_cifar.yml
@@ -36,7 +36,6 @@ job_conf:
     - data_dir: $FEDSCALE_HOME/benchmark/dataset/data/    # Path of the dataset
     - model: resnet50                    # Need to define the model in tf_aggregator.py
     - model_zoo: fedscale-tensorflow-zoo
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 5000                # How many rounds to run a testing on the testing set
     - rounds: 200                        # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                     # Remove clients w/ less than 21 samples

diff --git a/benchmark/configs/tf_femnist/tf_femnist.yml b/benchmark/configs/tf_femnist/tf_femnist.yml
@@ -36,7 +36,6 @@ job_conf:
     - data_dir: $FEDSCALE_HOME/benchmark/dataset/data/femnist    # Path of the dataset
     - model: resnet50                    # Need to define the model in tf_aggregator.py
     - model_zoo: fedscale-tensorflow-zoo
-#   - gradient_policy: fed-yogi                 # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default
     - eval_interval: 5000                # How many rounds to run a testing on the testing set
     - rounds: 200                        # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds
     - filter_less: 0                     # Remove clients w/ less than 21 samples