1 research outputs found
MDLdroid: a ChainSGD-reduce Approach to Mobile Deep Learning for Personal Mobile Sensing
Personal mobile sensing is fast permeating our daily lives to enable activity
monitoring, healthcare and rehabilitation. Combined with deep learning, these
applications have achieved significant success in recent years. Different from
conventional cloud-based paradigms, running deep learning on devices offers
several advantages including data privacy preservation and low-latency response
for both model inference and update. Since data collection is costly in
reality, Google's Federated Learning offers not only complete data privacy but
also better model robustness based on multiple user data. However, personal
mobile sensing applications are mostly user-specific and highly affected by
environment. As a result, continuous local changes may seriously affect the
performance of a global model generated by Federated Learning. In addition,
deploying Federated Learning on a local server, e.g., edge server, may quickly
reach the bottleneck due to resource constraint and serious failure by attacks.
Towards pushing deep learning on devices, we present MDLdroid, a novel
decentralized mobile deep learning framework to enable resource-aware on-device
collaborative learning for personal mobile sensing applications. To address
resource limitation, we propose a ChainSGD-reduce approach which includes a
novel chain-directed Synchronous Stochastic Gradient Descent algorithm to
effectively reduce overhead among multiple devices. We also design an
agent-based multi-goal reinforcement learning mechanism to balance resources in
a fair and efficient manner. Our evaluations show that our model training on
off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device
training, and 1.5x faster than the master-slave approach.Comment: Published in the International Conference on Information Processing
in Sensor Networks (IPSN), 202