litian96 / feddane Goto Github PK

Thank you so much for your efforts (papers + code).
There are few pieces I did not understand. Would you please help!

I noticed that in the FedDANE paper, it is mentioned in the algorithm that we first need to choose a subset S to compute the gradients, then we need to choose another subset S' to run the actual training (update clients weights). However, in your code I noticed that in FedDANE trainer you are passing the same seed:
selected_clients = self.select_clients(i, num_clients=self.clients_per_round)
line number 28 and 39 in FedDANE trainer. So you are choosing the same subset again not another subset S'.

Q2: In the algorithm, it is mentioned that the averaging will take place over the subset S not S' that we actually trained. So I was wondering is that a typo? If not, then would you please explain why we need to train S' then average another set S ?

Q3: When we run the first training loop to average the gradients, then we train for one epoch only, right? Since adding more than one epoch, will overwrite the gradients.

Q4: Finally, I believe in your code you assumed none of the devices will drop, is that correct?

Thank you so much for your time!