Poster Abstract: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing

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.