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Real-time Federated Evolutionary Neural Architecture Search
Federated learning is a distributed machine learning approach to privacy...
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A Survey on Neural Architecture Search
The growing interest in both the automation of machine learning and deep...
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Differentially-private Federated Neural Architecture Search
Neural architecture search, which aims to automatically search for archi...
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FedNAS: Federated Deep Learning via Neural Architecture Search
Federated Learning (FL) has been proved to be an effective learning fram...
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Direct Federated Neural Architecture Search
Neural Architecture Search (NAS) is a collection of methods to craft the...
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TreeGrad: Transferring Tree Ensembles to Neural Networks
Gradient Boosting Decision Tree (GBDT) are popular machine learning algo...
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FDNAS: Improving Data Privacy and Model Diversity in AutoML
To prevent the leakage of private information while enabling automated m...
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From Federated Learning to Federated Neural Architecture Search: A Survey
Federated learning is a recently proposed distributed machine learning paradigm for privacy preservation, which has found a wide range of applications where data privacy is of primary concern. Meanwhile, neural architecture search has become very popular in deep learning for automatically tuning the architecture and hyperparameters of deep neural networks. While both federated learning and neural architecture search are faced with many open challenges, searching for optimized neural architectures in the federated learning framework is particularly demanding. This survey paper starts with a brief introduction to federated learning, including both horizontal, vertical, and hybrid federated learning. Then, neural architecture search approaches based on reinforcement learning, evolutionary algorithms and gradient-based are presented. This is followed by a description of federated neural architecture search that has recently been proposed, which is categorized into online and offline implementations, and single- and multi-objective search approaches. Finally, remaining open research questions are outlined and promising research topics are suggested.
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