From 7918a28f294811ee1409320c1cccd7a6b37c8bb4 Mon Sep 17 00:00:00 2001 From: EricDinging Date: Sun, 17 Dec 2023 08:05:52 -0500 Subject: [PATCH] Validated; change optimizer naming in config --- benchmark/configs/openimage/openimage.yml | 2 +- benchmark/configs/others/local_dp.yml | 2 +- benchmark/configs/others/rl_conf.yml | 2 +- benchmark/configs/speech/google_speech.yml | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/benchmark/configs/openimage/openimage.yml b/benchmark/configs/openimage/openimage.yml index 829d3d93..96feb4e0 100644 --- a/benchmark/configs/openimage/openimage.yml +++ b/benchmark/configs/openimage/openimage.yml @@ -51,7 +51,7 @@ 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: shufflenet_v2_x2_0 # 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 + - 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/others/local_dp.yml b/benchmark/configs/others/local_dp.yml index 3c4c3747..78dc0c8e 100644 --- a/benchmark/configs/others/local_dp.yml +++ b/benchmark/configs/others/local_dp.yml @@ -38,7 +38,7 @@ job_conf: # - device_avail_file: /users/JIACHEN/FLPerf-Cluster/client_datamap/client_behave_trace - sample_mode: random # Client selection: random, oort, random by default - model: resnet18 # Models: shufflenet_v2_x2_0, mobilenet_v2, resnet34, albert-base-v2 - - gradient_policy: fedavg # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default + - gradient_policy: fed-avg # {"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: 500 # Number of rounds to run this training. We use 1000 in our paper, while it may converge w/ ~400 rounds - filter_less: 20 diff --git a/benchmark/configs/others/rl_conf.yml b/benchmark/configs/others/rl_conf.yml index bf8e04aa..b069cb5e 100644 --- a/benchmark/configs/others/rl_conf.yml +++ b/benchmark/configs/others/rl_conf.yml @@ -48,7 +48,7 @@ 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: dqn # 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 + - gradient_policy: fed-yogi # {"fed-yogi", "fed-prox", "fed-avg"}, "fed-avg" by default - eval_interval: 50 # 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/speech/google_speech.yml b/benchmark/configs/speech/google_speech.yml index 8d4696f5..a7875f74 100644 --- a/benchmark/configs/speech/google_speech.yml +++ b/benchmark/configs/speech/google_speech.yml @@ -50,7 +50,7 @@ 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: resnet34 # 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 + - 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