Neural Architecture Search over Decentralized Data

By Mengwei Xu, Yuxin Zhao, Kaigui Bian, Gang Huang, Qiaozhu Mei, Xuanzhe Liu. 2020

7 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Chaoyang He
2. Yang Liu
3. Murali Annavaram
4. Hangyu Zhu
5. Xinle Liang
6. Qinbin Li
7. Qiang Yang
8. Jinhyun So

Citations in the corpus

• FedML: A Research Library and Benchmark for Federated Machine Learning (2020, background): “In terms of exchanged information, aside from exchanging gradients and models, recent FL algorithms propose to exchange information such as pseudo labels in semi-supervised FL [56] and architecture parameters in neural architecture search-based FL [5, 57, 58].”
• Federated Learning on Non-IID Data: A Survey (2021, background): “However, only a limited amount of research on federated NAS has been reported [145, 121, 48, 169] and little work has considered the influence of Non-IID distributions.”
• Self-supervised Cross-silo Federated Neural Architecture Search (2021, background): “[23] presented FedNAS which introduced NAS into the federated learning system, with the considerations of several key optimizations, including communication costs and local computation costs.”
• Differentially-private Federated Neural Architecture Search (2020, methodology): “In (Xu et al., 2020), the search algorithm is based on NetAdapt (Yang et al., 2018), which adapts a pretrained model to a new hardware platform, where the performance of the searched architecture is limited to that of the pre-trained model.”