机器学习小白来提问：关于联邦学习FedAVG和FedSGD的问题？ 第1页

参考FedSGD和FedAVG的原始论文《Federated Learning of Deep Networks using Model Averaging》中的一段话：

To apply this approach in the federated setting, we select a C-fraction of clients on each round, and compute the gradient of the loss over all the data held by these clients. Thus, C controls the global batch size, with C = 1 corresponding to full-batch (non-stochastic) gradient descent.2 We refer to this baseline algorithm as FederatedSGD (or FedSGD).

FedSGD：每次采用client的所有数据集进行训练，本地训练次数为1，然后进行aggregation。

C：the fraction of clients that perform computation on each round

每次参与联邦聚合的clients数量占client总数的比例。C=1 代表所有成员参与聚合

B：the local minibatch size used for the client updates.

client的本地的训练的batchsize

E：then number of training passes each client makes over its local dataset on each round

两次联邦训练之间的本地训练的次数

综上所述，C=E=1 ，B=∞时，FedAvg等价于FedSGD，FedSGD是特殊形式的FedAVG，与采用什么优化器没有关系。