flsim/servers/sync_secagg_servers.py [73:112]:
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        )
        self._active_user_selector = instantiate(self.cfg.active_user_selector)
        self._channel: IFLChannel = channel or IdentityChannel()

    @classmethod
    def _set_defaults_in_cfg(cls, cfg):
        if OmegaConf.is_missing(cfg.active_user_selector, "_target_"):
            cfg.active_user_selector = UniformlyRandomActiveUserSelectorConfig()
        if OmegaConf.is_missing(cfg.server_optimizer, "_target_"):
            cfg.server_optimizer = FedAvgOptimizerConfig()

    @property
    def global_model(self):
        return self._global_model

    def select_clients_for_training(
        self,
        num_total_users,
        users_per_round,
        data_provider: Optional[IFLDataProvider] = None,
        epoch: Optional[int] = None,
    ):
        return self._active_user_selector.get_user_indices(
            num_total_users=num_total_users,
            users_per_round=users_per_round,
            data_provider=data_provider,
            global_model=self.global_model,
            epoch=epoch,
        )

    def init_round(self):
        self._aggregator.zero_weights()
        self._optimizer.zero_grad()

    def receive_update_from_client(self, message: Message):
        message = self._channel.client_to_server(message)

        self._aggregator.apply_weight_to_update(
            delta=message.model.fl_get_module(), weight=message.weight
        )
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flsim/servers/sync_servers.py [127:166]:
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        )
        self._active_user_selector = instantiate(self.cfg.active_user_selector)
        self._channel: IFLChannel = channel or IdentityChannel()

    @classmethod
    def _set_defaults_in_cfg(cls, cfg):
        if OmegaConf.is_missing(cfg.active_user_selector, "_target_"):
            cfg.active_user_selector = UniformlyRandomActiveUserSelectorConfig()
        if OmegaConf.is_missing(cfg.server_optimizer, "_target_"):
            cfg.server_optimizer = FedAvgOptimizerConfig()

    @property
    def global_model(self):
        return self._global_model

    def select_clients_for_training(
        self,
        num_total_users,
        users_per_round,
        data_provider: Optional[IFLDataProvider] = None,
        epoch: Optional[int] = None,
    ):
        return self._active_user_selector.get_user_indices(
            num_total_users=num_total_users,
            users_per_round=users_per_round,
            data_provider=data_provider,
            global_model=self.global_model,
            epoch=epoch,
        )

    def init_round(self):
        self._aggregator.zero_weights()
        self._optimizer.zero_grad()

    def receive_update_from_client(self, message: Message):
        message = self._channel.client_to_server(message)

        self._aggregator.apply_weight_to_update(
            delta=message.model.fl_get_module(), weight=message.weight
        )
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