This is a reimplementation of FedAWS (Federated Learning with Only Positive Labels, ICML 2020) in PyTorch. The reimplementation algorithm comes from FedRepo. Basically, I just modified the code under feddata.py to make sure each client holds only positive label, and I also implement the loss function implemented in FedFace, which also uses the FedAWS algorithm.
However, the performance of this reimplementation is pretty bad. It can only achieve at most 17% accuracy on cifar-10 dataset.
I try to change the hyperparameters, but it doesn't work. I think the reason is that the this code doesn't implement the
the learning rate multiplier of the spreadout loss λ in the FedAWS algorithm, making the class embeddings are not spreadout.
But I don't know how to implement it. If you have any idea, please let me know. Thanks.
You can reach me by email: rcheng4@gmu.edu. I would love to discuss this algorithm and anything related to FL with you.
Just use python train_lab.py then you can run FedAWS under cifar-10 dataset.
You can change hyperparameters in train_lab.py.
Only cifar-10 is on this repo. But you can also use cifar-100 and FaMNIST datasets after you download them.
This repo is based on FedRepo. Thanks for the author's great work.