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Decentralized Collaborative Power Management through Multi-Device Knowledge Sharing
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DOI：10.1109/ICCD.2018.00068.png)
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Abstract
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Battery-powered mobile devices have limited energy capacity, urging the development of efficient power management approaches. Reinforcement learning (RL) algorithms are adaptive to the changing environment and have been widely used for runtime power management. Recently, collaborative RL-based approaches have been explored to accelerate the learning process. Nonetheless, existing works usually require a cloud service provider to achieve centralized multi-device knowledge sharing, which suffers from the single point of failure and does not guarantee users' privacy. To address this issue, we propose a decentralized multi-device collaborative power management approach in this work, where devices directly share their knowledge with their trusted neighbors. Experimental results show that the proposed method can achieve an up to 21% energy reduction with a 4x speedup over the individual learning-based approach.
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