# Copyright (c) Alibaba, Inc. and its affiliates. import unittest from federatedscope.core.auxiliaries.data_builder import get_data from federatedscope.core.auxiliaries.utils import setup_seed from federatedscope.core.auxiliaries.logging import update_logger from federatedscope.core.configs.config import global_cfg from federatedscope.core.auxiliaries.runner_builder import get_runner from federatedscope.core.auxiliaries.worker_builder import get_server_cls, get_client_cls class KrumAlgoTest(unittest.TestCase): def setUp(self): print(('Testing %s.%s' % (type(self).__name__, self._testMethodName))) def set_config_guassian_attack_no_defnece(self, cfg): backup_cfg = cfg.clone() import torch cfg.use_gpu = torch.cuda.is_available() cfg.device = 0 cfg.eval.freq = 20 cfg.eval.count_flops = False cfg.eval.metrics = ['acc', 'loss_regular'] cfg.federate.mode = 'standalone' cfg.train.local_update_steps = 2 cfg.federate.total_round_num = 20 cfg.federate.sample_client_num = 20 cfg.federate.client_num = 50 cfg.data.root = 'test_data/' cfg.data.type = 'femnist' cfg.data.splits = [0.6, 0.2, 0.2] cfg.data.batch_size = 10 cfg.data.subsample = 0.01 cfg.data.transform = [['ToTensor'], [ 'Normalize', { 'mean': [0.1307], 'std': [0.3081] } ]] cfg.model.type = 'convnet2' cfg.model.hidden = 512 cfg.model.out_channels = 62 cfg.train.optimizer.lr = 0.01 cfg.train.optimizer.weight_decay = 0.0 cfg.criterion.type = 'CrossEntropyLoss' cfg.trainer.type = 'cvtrainer' cfg.seed = 123 cfg.attack.attack_method = 'gaussian_noise' cfg.attack.attacker_id = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] return backup_cfg def set_config_guassian_attack_krum(self, cfg): backup_cfg = cfg.clone() import torch cfg.use_gpu = torch.cuda.is_available() cfg.device = 0 cfg.eval.freq = 50 cfg.eval.count_flops = False cfg.eval.metrics = ['acc', 'loss_regular'] cfg.federate.mode = 'standalone' cfg.train.local_update_steps = 2 cfg.federate.total_round_num = 20 cfg.federate.sample_client_num = 20 cfg.federate.client_num = 50 cfg.data.root = 'test_data/' cfg.data.type = 'femnist' cfg.data.splits = [0.6, 0.2, 0.2] cfg.data.batch_size = 10 cfg.data.subsample = 0.01 cfg.data.transform = [['ToTensor'], [ 'Normalize', { 'mean': [0.1307], 'std': [0.3081] } ]] cfg.model.type = 'convnet2' cfg.model.hidden = 512 cfg.model.out_channels = 62 cfg.train.optimizer.lr = 0.01 cfg.train.optimizer.weight_decay = 0.0 cfg.criterion.type = 'CrossEntropyLoss' cfg.trainer.type = 'cvtrainer' cfg.seed = 123 cfg.attack.attack_method = 'gaussian_noise' cfg.attack.attacker_id = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] cfg.aggregator.byzantine_node_num = 10 cfg.aggregator.robust_rule = 'krum' cfg.aggregator.BFT_args.krum_agg_num = 1 return backup_cfg def set_config_guassian_attack_multi_krum(self, cfg): backup_cfg = cfg.clone() import torch cfg.use_gpu = torch.cuda.is_available() cfg.device = 0 cfg.eval.freq = 30 cfg.eval.count_flops = False cfg.eval.metrics = ['acc', 'loss_regular'] cfg.federate.mode = 'standalone' cfg.train.local_update_steps = 2 cfg.federate.total_round_num = 20 cfg.federate.sample_client_num = 20 cfg.federate.client_num = 50 cfg.data.root = 'test_data/' cfg.data.type = 'femnist' cfg.data.splits = [0.6, 0.2, 0.2] cfg.data.batch_size = 10 cfg.data.subsample = 0.01 cfg.data.transform = [['ToTensor'], [ 'Normalize', { 'mean': [0.1307], 'std': [0.3081] } ]] cfg.model.type = 'convnet2' cfg.model.hidden = 512 cfg.model.out_channels = 62 cfg.train.optimizer.lr = 0.01 cfg.train.optimizer.weight_decay = 0.0 cfg.criterion.type = 'CrossEntropyLoss' cfg.trainer.type = 'cvtrainer' cfg.seed = 123 cfg.attack.attack_method = 'gaussian_noise' cfg.attack.attacker_id = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10] cfg.aggregator.byzantine_node_num = 10 cfg.aggregator.robust_rule = 'krum' cfg.aggregator.BFT_args.krum_agg_num = 5 return backup_cfg def test_guassian_attack_no_defnece(self): init_cfg = global_cfg.clone() backup_cfg = self.set_config_guassian_attack_no_defnece(init_cfg) setup_seed(init_cfg.seed) update_logger(init_cfg, True) data, modified_cfg = get_data(init_cfg.clone()) init_cfg.merge_from_other_cfg(modified_cfg) self.assertIsNotNone(data) Fed_runner = get_runner(data=data, server_class=get_server_cls(init_cfg), client_class=get_client_cls(init_cfg), config=init_cfg.clone()) self.assertIsNotNone(Fed_runner) test_best_results = Fed_runner.run() print(test_best_results) init_cfg.merge_from_other_cfg(backup_cfg) self.assertLess( test_best_results['client_summarized_weighted_avg']['test_acc'], 0.1) init_cfg.merge_from_other_cfg(backup_cfg) def test_guassian_attack_krum(self): init_cfg = global_cfg.clone() backup_cfg = self.set_config_guassian_attack_krum(init_cfg) setup_seed(init_cfg.seed) update_logger(init_cfg, True) data, modified_cfg = get_data(init_cfg.clone()) init_cfg.merge_from_other_cfg(modified_cfg) self.assertIsNotNone(data) Fed_runner = get_runner(data=data, server_class=get_server_cls(init_cfg), client_class=get_client_cls(init_cfg), config=init_cfg.clone()) self.assertIsNotNone(Fed_runner) test_best_results = Fed_runner.run() print(test_best_results) init_cfg.merge_from_other_cfg(backup_cfg) self.assertGreater( test_best_results['client_summarized_weighted_avg']['test_acc'], 0.15) init_cfg.merge_from_other_cfg(backup_cfg) def test_guassian_attack_multi_krum(self): init_cfg = global_cfg.clone() backup_cfg = self.set_config_guassian_attack_multi_krum(init_cfg) setup_seed(init_cfg.seed) update_logger(init_cfg, True) data, modified_cfg = get_data(init_cfg.clone()) init_cfg.merge_from_other_cfg(modified_cfg) self.assertIsNotNone(data) Fed_runner = get_runner(data=data, server_class=get_server_cls(init_cfg), client_class=get_client_cls(init_cfg), config=init_cfg.clone()) self.assertIsNotNone(Fed_runner) test_best_results = Fed_runner.run() print(test_best_results) init_cfg.merge_from_other_cfg(backup_cfg) self.assertGreater( test_best_results['client_summarized_weighted_avg']['test_acc'], 0.2) init_cfg.merge_from_other_cfg(backup_cfg) if __name__ == '__main__': unittest.main()