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: [email protected]. 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.

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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.