  With the prosperity of mobile devices, the distributed learning approach
enabling model training with decentralized data has attracted wide research.
However, the lack of training capability for edge devices significantly limits
the energy efficiency of distributed learning in real life. This paper
describes a novel approach of training DNNs exploiting the redundancy and the
weight asymmetry potential of conventional backpropagation. We demonstrate that
with negligible classification accuracy loss, the proposed approach outperforms
the prior arts by 5x in terms of energy efficiency.
