FedAvg pytorch

FedAvg pytorch是一个用于联邦学习的算法。它通过对参与者的本地模型进行加权平均来实现全局模型的更新。这个算法的实现非常简单，它首先对每个参与者的模型参数进行平均，然后将平均参数作为全局模型的更新。具体代码如下所示：
def FedAvg(w):
w_avg = copy.deepcopy(w += w[i][k
w_avg[k = torch.true_divide(w_avg[k], len(w))
return w_avg
其中，w是一个包含参与者模型参数的列表。算法遍历每个参数的键值对，将所有参与者的对应参数加和，并将结果除以参与者的数量，得到平均参数作为全局模型的更新。这样，通过不同参与者的贡献，全局模型可以得到更新并获得更好的性能。<span class="em">1</span><span class="em">2</span><span class="em">3</span>
#### 引用[.reference_title]
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