Research Article
Local Epochs Inefficiency Caused by Device Heterogeneity in Federated Learning
Algorithm 2
Dynamically set number of local epochs.
Input: The clients are indexed by ; B local minibatch size, T Coomunication time window, M time prediction model. | 1: Server executes: | 2: initialize | 3: for each round t, t =1,2,…,Ndo | 4: | 5: | 6: for each client in parallel: do | 7: ClientUpdate | 8: end for | 9: | 10: end for | 11: ClientUpdate | 12: // Client receives the global model | 13: (number of batches per epoch divided by B) | 14: | 15: GetTrainingTime(M, f) | 16: | 17: for each epoch from e to do | 18: for each batch from k to do | 19: | 20: | 21: | 22: end for | 23: end for | 24: return to the Server |
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