Poster Abstract: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
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| Názov: | Poster Abstract: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing |
|---|---|
| Autori: | Yu Zhang, Tao Gu, Xi Zhang |
| Rok vydania: | 2020 |
| Predmety: | Machine learning not elsewhere classified, Distribute computing, Mobile deep learning, Neural networks, Reinforcement learning, Resource allocation |
| Popis: | MDLdroid is a novel decentralized mobile deep learning framework, which enables resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, MDLdroid uses a chain-directed Synchronous Stochastic Gradient Descent (ChainSGD-reduce) approach to effectively reduce overhead among multiple devices. In addition, MDLdroid includes an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Real-world experiments demonstrate that our model training on off-the-shelf mobile devices achieves 2× to 3.5× faster than single-device training, and 1.5× faster than the master-slave approach. |
| Druh dokumentu: | conference object |
| Jazyk: | unknown |
| Relation: | 10779/rmit.27589056.v1 |
| Dostupnosť: | https://figshare.com/articles/conference_contribution/Poster_Abstract_a_ChainSGD-reduce_Approach_to_Mobile_Deep_Learning_for_Personal_Mobile_Sensing/27589056 |
| Rights: | All rights reserved |
| Prístupové číslo: | edsbas.81C7BCC4 |
| Databáza: | BASE |
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