FedPAQ-MNIST-实施:使用不同实验参数的FedPAQ实施。 我们将研究方法(r(要选择的客户数量),t(本地时期)和s(量化级别))的不同变化。-源码

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FedPAQ-MNIST实现 使用不同的实验参数实现FedPAQ。 我们将研究方法(r(要选择的客户数量),t(本地时期)和精度(量化级别))的不同变化。 在这里,我们清楚地了解了local epochs在1和root(T)之间(此处为10)的重要性。 我们可以看到,最佳local epoch是3 ,它是最接近的根(10),同时,当比较1和5时,我们看到1具有更好的性能,因此具有学习潜力。 因此,在实验上支持理想的局部历元数应在1到root(T) (或root(10)〜3)之间的主张。 至于r的值,在此实现中,我使用了相对于客户总数的比率。 这样做是为了更容易理解。 我们也看到在加大倾斜r如r增加,从0.5到1 。 FedPAQ声称理想性能将由r = 100%得到支持。 最后,此处以形式精度启用量化,以使初学者和非编码人员都可以理解。 这仍然是由FedPAQ与问候量化等级(要求支持s

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