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ADAPTIVE STOCHASTIC LEARNING STATE COMPRESSION FOR FEDERATED LEARNING IN INFRASTRUCTURE DOMAINS

机译:基础设施域中联合学习的自适应随机学习状态压缩

摘要

A method for adaptive stochastic learning state compression for federated learning in infrastructure domains. Specifically, the disclosed method introduces an adaptive data compressor directed to reducing the amount of information exchanged between nodes participating in the optimization of a shared machine learning model through federated learning. The adaptive data compressor may employ stochastic k-level quantization, and may include functionality to handle exceptions stemming from the detection of unbalanced and/or irregularly sized data.
机译:基础设施域联合学习的自适应随机学习状态压缩方法。 具体地,所公开的方法介绍了一种自适应数据压缩机,其指向通过联合学习参与参与共享机器学习模型的优化的节点之间交换的信息量。 自适应数据压缩机可以采用随机k电平量化,并且可以包括处理从检测到不平衡和/或不规则尺寸的数据的干扰的异常的功能。

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