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GG网络技术分享 2025-11-12 21:59 3
python import tensorflow as tf import tensorflow_federated as tff
source, _ = tff.simulation.datasets.cifar10.load_data
def preprocess: def element_fn: return element, element return dataset.repeat.map.batch
def makefederateddata: return
def createcompiledkeras_model: model = tf.keras.models.Sequential() model.compile( loss=tf.keras.losses.SparseCategoricalCrossentropy, optimizer=tf.keras.optimizers.SGD, metrics= ) return model
@tff.federatedcomputation def serverinit: return tff.learning.fromcompiledkeras_model)
def createfederatedaveragingprocess: modelfn = createcompiledkerasmodel serveroptimizerfn = lambda: tf.keras.optimizers.SGD clientweightfn = None return tff.learning.buildfederatedaveragingprocess( modelfn=modelfn, serveroptimizerfn=serveroptimizerfn, clientweightfn=clientweightfn )
clientids = federatedtraindata = makefederateddata
iterativeprocess = createfederatedaveragingprocess state = iterative_process.initialize print
for roundnum in range: # Just for demonstration; typically you would do many rounds. state, metrics = iterativeprocess.next print print
在这玩意儿示例中，我们用了CIFAR-10数据集，并来训练一个轻巧松的卷积神经网络模型。个个客户端在本地训练模型，然后将geng新鲜的模型参数上传到服务器。服务器聚合全部客户端的参数，并geng新鲜全局模型。这玩意儿过程再来一次进行，直到达到预设的轮数或者达到满意的模型性Neng。
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