143 lines
5.5 KiB
Python
143 lines
5.5 KiB
Python
from federatedscope.core.configs.config import CN
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from federatedscope.core.configs.yacs_config import Argument
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from federatedscope.register import register_config
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def extend_fl_algo_cfg(cfg):
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# ---------------------------------------------------------------------- #
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# fedopt related options, a general fl algorithm
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# ---------------------------------------------------------------------- #
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cfg.fedopt = CN()
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cfg.fedopt.use = False
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cfg.fedopt.optimizer = CN(new_allowed=True)
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cfg.fedopt.optimizer.type = Argument(
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'SGD', description="optimizer type for FedOPT")
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cfg.fedopt.optimizer.lr = Argument(
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0.01, description="learning rate for FedOPT optimizer")
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cfg.fedopt.annealing = False
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cfg.fedopt.annealing_step_size = 2000
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cfg.fedopt.annealing_gamma = 0.5
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# ---------------------------------------------------------------------- #
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# fedprox related options, a general fl algorithm
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# ---------------------------------------------------------------------- #
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cfg.fedprox = CN()
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cfg.fedprox.use = False
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cfg.fedprox.mu = 0.
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# ---------------------------------------------------------------------- #
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# fedswa related options, Stochastic Weight Averaging (SWA)
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# ---------------------------------------------------------------------- #
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cfg.fedswa = CN()
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cfg.fedswa.use = False
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cfg.fedswa.freq = 10
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cfg.fedswa.start_rnd = 30
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# ---------------------------------------------------------------------- #
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# Personalization related options, pFL
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# ---------------------------------------------------------------------- #
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cfg.personalization = CN()
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# client-distinct param names, e.g., ['pre', 'post']
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cfg.personalization.local_param = []
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cfg.personalization.share_non_trainable_para = False
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cfg.personalization.local_update_steps = -1
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# @regular_weight:
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# The smaller the regular_weight is, the stronger emphasising on
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# personalized model
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# For Ditto, the default value=0.1, the search space is [0.05, 0.1, 0.2,
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# 1, 2]
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# For pFedMe, the default value=15
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cfg.personalization.regular_weight = 0.1
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# @lr:
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# 1) For pFedME, the personalized learning rate to calculate theta
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# approximately using K steps
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# 2) 0.0 indicates use the value according to optimizer.lr in case of
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# users have not specify a valid lr
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cfg.personalization.lr = 0.0
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cfg.personalization.K = 5 # the local approximation steps for pFedMe
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cfg.personalization.beta = 1.0 # the average moving parameter for pFedMe
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# parameters for FedRep：
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cfg.personalization.lr_feature = 0.1 # learning rate: feature extractors
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cfg.personalization.lr_linear = 0.1 # learning rate: linear head
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cfg.personalization.epoch_feature = 1 # training epoch number
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cfg.personalization.epoch_linear = 2 # training epoch number
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cfg.personalization.weight_decay = 0.0
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# ---------------------------------------------------------------------- #
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# FedSage+ related options, gfl
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# ---------------------------------------------------------------------- #
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cfg.fedsageplus = CN()
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# Number of nodes generated by the generator
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cfg.fedsageplus.num_pred = 5
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# Hidden layer dimension of generator
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cfg.fedsageplus.gen_hidden = 128
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# Hide graph portion
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cfg.fedsageplus.hide_portion = 0.5
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# Federated training round for generator
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cfg.fedsageplus.fedgen_epoch = 200
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# Local pre-train round for generator
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cfg.fedsageplus.loc_epoch = 1
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# Coefficient for criterion number of missing node
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cfg.fedsageplus.a = 1.0
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# Coefficient for criterion feature
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cfg.fedsageplus.b = 1.0
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# Coefficient for criterion classification
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cfg.fedsageplus.c = 1.0
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# ---------------------------------------------------------------------- #
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# GCFL+ related options, gfl
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# ---------------------------------------------------------------------- #
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cfg.gcflplus = CN()
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# Bound for mean_norm
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cfg.gcflplus.EPS_1 = 0.05
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# Bound for max_norm
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cfg.gcflplus.EPS_2 = 0.1
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# Length of the gradient sequence
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cfg.gcflplus.seq_length = 5
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# Whether standardized dtw_distances
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cfg.gcflplus.standardize = False
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# ---------------------------------------------------------------------- #
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# FLIT+ related options, gfl
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# ---------------------------------------------------------------------- #
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cfg.flitplus = CN()
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cfg.flitplus.tmpFed = 0.5 # gamma in focal loss (Eq.4)
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cfg.flitplus.lambdavat = 0.5 # lambda in phi (Eq.10)
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cfg.flitplus.factor_ema = 0.8 # beta in omega (Eq.12)
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cfg.flitplus.weightReg = 1.0 # balance lossLocalLabel and lossLocalVAT
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# --------------- register corresponding check function ----------
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cfg.register_cfg_check_fun(assert_fl_algo_cfg)
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def assert_fl_algo_cfg(cfg):
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if cfg.personalization.local_update_steps == -1:
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# By default, use the same step to normal mode
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cfg.personalization.local_update_steps = \
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cfg.train.local_update_steps
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cfg.personalization.local_update_steps = \
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cfg.train.local_update_steps
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if cfg.personalization.lr <= 0.0:
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# By default, use the same lr to normal mode
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cfg.personalization.lr = cfg.train.optimizer.lr
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if cfg.fedswa.use:
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assert cfg.fedswa.start_rnd < cfg.federate.total_round_num, \
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f'`cfg.fedswa.start_rnd` {cfg.fedswa.start_rnd} must be smaller ' \
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f'than `cfg.federate.total_round_num` ' \
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f'{cfg.federate.total_round_num}.'
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register_config("fl_algo", extend_fl_algo_cfg)
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