Abstract
Federated Learning [1] enables distributed devices to learn a shared machine learning model together, without uploading their private training data. It has received significant attention recently and has been used in mobile applications such as search suggestion [2] and object detection [3]. Federated Learning is different from distributed machine learning due to the following reasons: 1) System heterogeneity: federated learning is usually performed on devices having highly dynamic and heterogeneous network, compute, and power availability. 2) Data heterogeneity (or statistical heterogeneity): data is produced by different users on different devices, and therefore may have different statistical distribution (non-IID).
Original language | English (US) |
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Title of host publication | Proceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 164-166 |
Number of pages | 3 |
ISBN (Electronic) | 9781728159430 |
DOIs | |
State | Published - Nov 2020 |
Event | 5th IEEE/ACM Symposium on Edge Computing, SEC 2020 - Virtual, San Jose, United States Duration: Nov 11 2020 → Nov 13 2020 |
Publication series
Name | Proceedings - 2020 IEEE/ACM Symposium on Edge Computing, SEC 2020 |
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Conference
Conference | 5th IEEE/ACM Symposium on Edge Computing, SEC 2020 |
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Country/Territory | United States |
City | Virtual, San Jose |
Period | 11/11/20 → 11/13/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.