config.py

# GLOBAL PARAMETERS
DATASETS = ['sent140', 'nist', 'shakespeare',
            'mnist', 'synthetic', 'cifar10','mqtt']
TRAINERS = {'fedavg': 'FedAvgTrainer',
            'fedavg4': 'FedAvg4Trainer',
            'fedavg5': 'FedAvg5Trainer',
            'fedavg9': 'FedAvg9Trainer',}
OPTIMIZERS = TRAINERS.keys()
BATCH_LIST = [32, 64, 128, 256, 32, 64, 128, 256,32, 64, 128, 256,32, 64, 128, 256,32, 64, 128, 256]
# SERVER_ADDR= 'localhost'   # When running in a real distributed setting, change to the server's IP address
SERVER_ADDR= '169.254.231.192'   # When running in a real distributed setting, change to the server's IP address
SERVER_PORT = 51000


class ModelConfig(object):
    def __init__(self):
        pass

    def __call__(self, dataset, model):
        dataset = dataset.split('_')[0]
        if dataset == 'mnist' or dataset == 'nist':
            if model == 'logistic' or model == '2nn':
                return {'input_shape': 784, 'num_class': 10}
            else:
                return {'input_shape': (1, 28, 28), 'num_class': 10}
        elif dataset == 'cifar10':
            return {'input_shape': (3, 32, 32), 'num_class': 10}
        elif dataset == 'sent140':
            sent140 = {'bag_dnn': {'num_class': 2},
                       'stacked_lstm': {'seq_len': 25, 'num_class': 2, 'num_hidden': 100},
                       'stacked_lstm_no_embeddings': {'seq_len': 25, 'num_class': 2, 'num_hidden': 100}
                       }
            return sent140[model]
        elif dataset == 'shakespeare':
            shakespeare = {'stacked_lstm': {'seq_len': 80, 'emb_dim': 80, 'num_hidden': 256}
                           }
            return shakespeare[model]
        elif dataset == 'synthetic':
            return {'input_shape': 60, 'num_class': 10}
        elif dataset == 'mqtt':
            return {'input_shape': 28, 'num_class': 5}
        else:
            raise ValueError('Not support dataset {}!'.format(dataset))


MODEL_PARAMS = ModelConfig()