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- from peft import (
- set_peft_model_state_dict,
- )
- import torch
- import os
- from torch.nn.functional import normalize
- def FedAvg(model, selected_clients_set, output_dir, local_dataset_len_dict, epoch):
- weights_array = normalize(
- torch.tensor([local_dataset_len_dict[client_id] for client_id in selected_clients_set],
- dtype=torch.float32),
- p=1, dim=0)
- for k, client_id in enumerate(selected_clients_set):
- single_output_dir = os.path.join(output_dir, str(epoch), "local_output_{}".format(client_id),
- "pytorch_model.bin")
- single_weights = torch.load(single_output_dir)
- if k == 0:
- weighted_single_weights = {key: single_weights[key] * (weights_array[k]) for key in
- single_weights.keys()}
- else:
- weighted_single_weights = {key: weighted_single_weights[key] + single_weights[key] * (weights_array[k])
- for key in
- single_weights.keys()}
- set_peft_model_state_dict(model, weighted_single_weights, "default")
- return model
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