How to set configuration#
In this notebook, we will provide details about how to set configurations for federated learning experiments.
Load default configuration#
APPFL empolys OmegaConf package, a hierarchical configuration system, for FL training configurations. OmegaConf
package allows users to create a hierarchical configuration in DictConfig
type from a python @dataclass
. For example, we can load the APPFL default configuration dataclass using OmegaConf.structured()
as shown below
[1]:
from appfl.config import Config
from omegaconf import OmegaConf
cfg = OmegaConf.structured(Config)
The configuration cfg
is initialized with the default values. Let’s check the configuration values.
[2]:
print(OmegaConf.to_yaml(cfg))
fed:
type: federated
servername: ServerFedAvg
clientname: ClientOptim
args:
server_learning_rate: 0.01
server_adapt_param: 0.001
server_momentum_param_1: 0.9
server_momentum_param_2: 0.99
optim: SGD
num_local_epochs: 10
optim_args:
lr: 0.001
use_dp: false
epsilon: 1
clip_grad: false
clip_value: 1
clip_norm: 1
device: cpu
device_server: cpu
num_clients: 1
num_epochs: 2
num_workers: 0
batch_training: true
train_data_batch_size: 64
train_data_shuffle: true
validation: true
test_data_batch_size: 64
test_data_shuffle: false
data_sanity: false
reproduce: true
pca_dir: ''
params_start: 0
params_end: 49
ncomponents: 40
use_tensorboard: false
load_model: false
load_model_dirname: ''
load_model_filename: ''
save_model: false
save_model_dirname: ''
save_model_filename: ''
checkpoints_interval: 2
save_model_state_dict: false
send_final_model: false
output_dirname: output
output_filename: result
logginginfo: {}
summary_file: ''
personalization: false
p_layers: []
config_name: ''
max_message_size: 104857600
operator:
id: 1
server:
id: 1
host: localhost
port: 50051
use_tls: false
api_key: null
client:
id: 1
enable_compression: false
lossy_compressor: SZ2
lossless_compressor: blosc
compressor_sz2_path: ../.compressor/SZ/build/sz/libSZ.dylib
compressor_sz3_path: ../.compressor/SZ3/build/tools/sz3c/libSZ3c.dylib
compressor_szx_path: ../.compressor/SZx-main/build/lib/libSZx.dylib
error_bounding_mode: ''
error_bound: 0.0
flat_model_dtype: np.float32
param_cutoff: 1024
Most variables are self-explanatory. Specifically,
Variable
fed
sets the choice of FL algorithm and the algorithm-related parameters, and it is aslo defined as a python@dataclass
. We provide the definition of those dataclasses atappfl.config.fed.*
. In details,appfl.config.fed.federated
is a general dataclass for all synchronous FL algorithms, where you can specify the server algorithm name atservername
, client algorithm name atclientname
, and all related arguments and paramters atargs
.appfl.config.fed.fedasync
is a general dataclass for all asynchronous FL algorithms, whoseargs
contains commonly-used parameters in asynchrnous FL.appfl.config.fed.iceadmm
is a dataclass specifically wrote for the ICEADMM privacy-preserving FL algorithm, whoseargs
contains all needed parameters for the ICEADMM algorithm.appfl.config.fed.iiadmm
is a dataclass specifically wrote for the IIADMM privacy-preserving FL algorithm, whoseargs
contains all needed parameters for the IIADMM algorithm.
Initialize configuration with arguments#
We can also initialize the configuration with other values. For example, the following code is loading the configuration with the algorithm choice of IIADMM
.
[3]:
from appfl.config import fed
cfg = OmegaConf.structured(Config(
fed = fed.iiadmm.IIADMM()
))
print(OmegaConf.to_yaml(cfg.fed))
type: iiadmm
servername: IIADMMServer
clientname: IIADMMClient
args:
num_local_epochs: 1
accum_grad: true
coeff_grad: false
optim: SGD
optim_args:
lr: 0.01
init_penalty: 100.0
residual_balancing:
res_on: false
res_on_every_update: false
tau: 1.1
mu: 10
use_dp: false
epsilon: 1
clip_grad: false
clip_value: 1
clip_norm: 1
Change configuration values#
We can also change the configuration value after initialization. For example, we can change fed
variable as follows:
[4]:
cfg = OmegaConf.structured(Config)
my_fed = OmegaConf.structured(fed.fedasync.FedAsync)
cfg.fed = my_fed
print(OmegaConf.to_yaml(cfg.fed))
type: fedasync
servername: ServerFedAsynchronous
clientname: ClientOptim
args:
server_learning_rate: 0.01
server_adapt_param: 0.001
server_momentum_param_1: 0.9
server_momentum_param_2: 0.99
optim: SGD
num_local_epochs: 10
optim_args:
lr: 0.001
use_dp: false
epsilon: 1
clip_grad: false
clip_value: 1
clip_norm: 1
K: 3
alpha: 0.9
staleness_func:
name: constant
args:
a: 0.5
b: 4
gradient_based: false