研究人员开发了一种新的类型化张量语言，用于形式化联邦学习和分析的结构。该语言区分了分布在客户端上的联邦张量和全局可用的共享张量。一项关键发现是共享状态分解理论，证明了一轮联邦程序可以通过与客户端数量无关的固定维度的共享状态进行分解。 AI
影响 形式化联邦学习计算，可能实现更高效和可扩展的分布式AI模型训练。
排序理由 该集群包含一篇关于联邦学习新形式化语言的学术论文。
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
新的类型化张量语言将联邦学习结构形式化
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报道来源 [2]
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面向联邦学习的类型化张量语言
Federated learning and analytics are often described as collections of separate protocols, even when they share the same mathematical form: client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor langu…
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一种用于联邦学习的类型化张量语言
Federated learning and analytics are often described as collections of separate protocols, even when they share the same mathematical form: client-local tensor computation, mergeable aggregation into shared state, and shared-only post-processing. We introduce a typed tensor langu…