lee-gihun / fedntd Goto Github PK

def get_dataloader_cifar100(root, train=True, batch_size=50, dataidxs=None):

missing return statement......

Hi, amazing work! However, have you ever test the setting when the number of clients is 10. I found that the NTD didn't show better performance when compared with Fedavg when the clients are ten.

Thanks for your great work and great code!

I reproduce the experiments on MNIST under NIID Partition Strategy : Sharding with FedAvg and Fedprox. I modify the dataset name to mnist in both fedavg.json and fedprox.json, and the local_epochs is modified to 3. I got the [Server Stat] Acc = 86 at round 67 for FedAvg and got [Server Stat] Acc = 90 at round 67 for Fedprox, which is obviously higher than their reported in Tab.1 of your paper.

So my question is how the results in Table 1 were obtained? Were the results obtained by averaging the performance of each round instead of selecting the best acc in a certain round?

In fact, I am new to federated learning and not very familiar with the methods of performance calculation. I appreciate your guidance.

Hi, I wonder whether the training data is transformed twice?