1 research outputs found
DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services
With the increasing artificial intelligence application, deep neural network
(DNN) has become an emerging task. However, to train a good deep learning model
will suffer from enormous computation cost and energy consumption. Recently,
blockchain has been widely used, and during its operation, a huge amount of
computation resources are wasted for the Proof of Work (PoW) consensus. In this
paper, we propose DLBC to exploit the computation power of miners for deep
learning training as proof of useful work instead of calculating hash values.
it distinguishes itself from recent proof of useful work mechanisms by
addressing various limitations of them. Specifically, DLBC handles multiple
tasks, larger model and training datasets, and introduces a comprehensive
ranking mechanism that considers tasks difficulty(e.g., model complexity,
network burden, data size, queue length). We also applied DNN-watermark [1] to
improve the robustness. In Section V, the average overhead of digital signature
is 1.25, 0.001, 0.002 and 0.98 seconds, respectively, and the average overhead
of network is 3.77, 3.01, 0.37 and 0.41 seconds, respectively. Embedding a
watermark takes 3 epochs and removing a watermark takes 30 epochs. This penalty
of removing watermark will prevent attackers from stealing, improving, and
resubmitting DL models from honest miners