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why the accuracy is so low? #6
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Did you run the original project, or re-implement the algorithm with your own codes? If you re-implement it, can you provide the accuracy of FedAvg and other baselines, check if the low ACC comes from the implementation? |
Thank you for responding. |
Got it. So the FedAvg has the similar acc with VHL in your case. What is the neural network you use? A simple CNN may not have enough capacity to fit real data and noise data. |
The VHL is strangely worse than FedAvg in my case. And I use the resnet18 to run. |
Could you try lr=0.1, or lr=0.3? And check the number of clients in total, number of clients each round, local epochs, the Non-IID degree of datasets. |
Thank you for responding. I will try it. |
I see. The accuracy of FedAvg can be improved further. You can try lr=0.3, or lr=0.1 with Momentum. And the first conv layer of resnet18 should be 3x3 instead of 7x7 used for ImageNet.
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ok, I will try later, thanks! |
Excuse me, I copy your code to my environment of experiment.
Firstly, the virtual data is generated in upsample way. Then, they are loaded and normalized by mean=(0.5, 0.5, 0.5), std=(0.25, 0.25, 0.25) to a Dataset. Lastly, the virtual dataloader is created by the virtual dataset. The batch_size and batch_num_per_class are 64, 20, respectively.
And the real dataset cifar10 is normalized by mean = [0.5071, 0.4865, 0.4409], std = [0.2673, 0.2564, 0.2762] like your code. And batch_size = 128
I set the weight of virtual data loss is 1, and the weight of proxy align loss is 0.5.
Why the accuracy is only 0.2 in the 100 round? If I ignored something? Could you help me to debug.
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