[feature] samplers #200
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[feature] samplers #200
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joneswong
reviewed
Jul 5, 2022
| idle_clients = np.nonzero(self.client_state)[0] | ||
| sampled_clients = np.random.choice(idle_clients, | ||
| size=size, | ||
| replace=False).tolist() |
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Is it possible to make this argument configurable?
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Do you mean replace? It should be set to False to avoid sampling repeated clients.
joneswong
reviewed
Jul 5, 2022
| def __init__(self, client_num): | ||
| self.client_state = np.asarray([1] * (client_num + 1)) | ||
| # Set the state of server (index=0) to 'working' | ||
| self.client_state[0] = 0 |
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it is very confusing. Server state is maintained in an array named "client_state"
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Since 'client_id' is numbered from 1 to M, index=0 for client_state is redundant. Although client_state[0] is used for server_state, it would not be modified during the training process.
Maybe we can declare it in the comments.
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The sampler is used to sample clients from the idle clients at each training round