Autofl: Enabling heterogeneity-aware energy efficient federated learning

YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - dl.acm.org
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …
被引用次数：73 相关文章
[PDF] nsf.gov

[PDF][PDF] AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - par.nsf.gov
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

AutoFL: Enabling heterogeneity-aware energy efficient federated learning

YG Kim, CJ Wu - 54th Annual IEEE/ACM International …, 2021 - asu.elsevierpure.com
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

YG Kim, CJ Wu - arXiv preprint arXiv:2107.08147, 2021 - arxiv.org
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

YG Kim, CJ Wu - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …