A review article that organizes communication-efficient and privacy-preserving on-device federated learning methods into three exchange modes and illustrates seven example frameworks.
No Peek: A Survey of private distributed deep learning
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abstract
We survey distributed deep learning models for training or inference without accessing raw data from clients. These methods aim to protect confidential patterns in data while still allowing servers to train models. The distributed deep learning methods of federated learning, split learning and large batch stochastic gradient descent are compared in addition to private and secure approaches of differential privacy, homomorphic encryption, oblivious transfer and garbled circuits in the context of neural networks. We study their benefits, limitations and trade-offs with regards to computational resources, data leakage and communication efficiency and also share our anticipated future trends.
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cs.IT 1years
2019 1verdicts
UNVERDICTED 1representative citing papers
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Distilling On-Device Intelligence at the Network Edge
A review article that organizes communication-efficient and privacy-preserving on-device federated learning methods into three exchange modes and illustrates seven example frameworks.