Comments (2)
CharlieDinh
commented on July 21, 2024
Hi, following the algorithm, and doing stochastic, for each epoch, we only sample one mini-batch, when the number of the local epoch is large enough, it will cover all batches in training data.
So by varying the number of the local epoch, actually it is similar to one epoch train complete data.
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josebummer
commented on July 21, 2024
Thank you very much for your prompt response. Perfect, I understand what you are saying.
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Related Issues (19)
- Experiment image
- Unable to generate non-iid MNIST Data HOT 4
- why train loss will be nan? HOT 1
- pfedme Optimizer Probelem HOT 6
- In the Per-FedAvg experiments, there is always an unignorable gap between the accuracy of the actual experimental results and the expected accuracy provided under the same conditions. HOT 5
- Something maybe wrong in data/mnist/generate_niid_20users.py HOT 1
- Some mistakes in generating niid mnist data HOT 11
- A question in PerAvg algorithm HOT 1
- Question about pFedMeOptimizer. HOT 2
- About the Hessian Approximation
- Hello author, why did Loss become Nan after more than a dozen rounds of training？
- some questions about the results HOT 1
- UserpFedMe class HOT 4
- A question about train_one_step() method. HOT 3
- Some questions about your peper and your code HOT 2
- Why does FedAvg only train on 1 batch in each local epoch? HOT 2
- Cifar-10 running error HOT 1
- Is Per-FedAvg implemented properly? HOT 11
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