计算机科学
约束(计算机辅助设计)
钥匙(锁)
移动设备
语言模型
联合学习
校长(计算机安全)
深度学习
人工智能
机器学习
人工神经网络
数据挖掘
计算机安全
机械工程
操作系统
工程类
作者
H. Brendan McMahan,Eider B Moore,Daniel Ramage,Blaise Agüera y Arcas
出处
期刊:Cornell University - arXiv
日期:2016-02-17
被引量:419
摘要
Modern mobile devices have access to a wealth of data suitable for learning models, which in turn can greatly improve the user experience on the device. For example, language models can improve speech recognition and text entry, and image models can automatically select good photos. However, this rich data is often privacy sensitive, large in quantity, or both, which may preclude logging to the data-center and training there using conventional approaches. We advocate an alternative that leaves the training data distributed on the mobile devices, and learns a shared model by aggregating locally-computed updates. We term this decentralized approach Federated Learning.
We present a practical method for the federated learning of deep networks that proves robust to the unbalanced and non-IID data distributions that naturally arise. This method allows high-quality models to be trained in relatively few rounds of communication, the principal constraint for federated learning. The key insight is that despite the non-convex loss functions we optimize, parameter averaging over updates from multiple clients produces surprisingly good results, for example decreasing the communication needed to train an LSTM language model by two orders of magnitude.
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