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With the increasing artificial intelligence application, deep neural network(DNN) has become an emerging task. However, to train a good deep learning modelwill suffer from enormous computation cost and energy consumption. Recently,blockchain has been widely used, and during its operation, a huge amount ofcomputation resources are wasted for the Proof of Work (PoW) consensus. In thispaper, we propose DLBC to exploit the computation power of miners for deeplearning training as proof of useful work instead of calculating hash values.it distinguishes itself from recent proof of useful work mechanisms byaddressing various limitations of them. Specifically, DLBC handles multipletasks, larger model and training datasets, and introduces a comprehensiveranking mechanism that considers tasks difficulty(e.g., model complexity,network burden, data size, queue length). We also applied DNN-watermark [1] toimprove the robustness. In Section V, the average overhead of digital signatureis 1.25, 0.001, 0.002 and 0.98 seconds, respectively, and the average overheadof network is 3.77, 3.01, 0.37 and 0.41 seconds, respectively. Embedding awatermark takes 3 epochs and removing a watermark takes 30 epochs. This penaltyof removing watermark will prevent attackers from stealing, improving, andresubmitting DL models from honest miners.

DLBC: A Deep Learning-Based Consensus in Blockchains for Deep Learning Services