The continuous thriving of the Blockchain society motivates research in novel designs of schemes supporting cryptocurrencies. Previously multiple Proof-of-Deep-Learning(PoDL) consensuses have been proposed to replace hashing with useful work such as deep learning model training tasks. The energy will be more efficiently used while maintaining the ledger. However deep learning models are problem-specific and can be extremely complex. Current PoDL consensuses still require much work to realize in the real world. In this paper, we proposed a novel consensus named Proof-of-Federated-Learning-Subchain(PoFLSC) to fill the gap. We applied a subchain to record the training, challenging, and auditing activities and emphasized the importance of valuable datasets in partner selection. We simulated 20 miners in the subchain to demonstrate the effectiveness of PoFLSC. When we reduce the pool size concerning the reservation priority order, the drop rate difference in the performance in different scenarios further exhibits that the miner with a higher Shapley Value (SV) will gain a better opportunity to be selected when the size of the subchain pool is limited. In the conducted experiments, the PoFLSC consensus supported the subchain manager to be aware of reservation priority and the core partition of contributors to establish and maintain a competitive subchain.

翻译：区块链社会的持续繁荣激发了支持加密货币的新型方案设计研究。此前已有多种基于深度学习证明（PoDL）共识被提出，旨在用深度学习模型训练等有益工作替代哈希计算。这种设计能在维护账本的同时更高效地利用能源。然而深度学习模型具有问题特异性且可能极其复杂，现有PoDL共识在实际应用中仍需大量改进。本文提出一种名为"基于联邦学习的子链证明（PoFLSC）"的新型共识机制以填补这一空白。我们构建了记录训练、挑战与审计活动的子链体系，并强调高价值数据集在合作伙伴选择中的关键作用。通过模拟子链中20个矿工节点验证了PoFLSC的有效性。实验表明：当根据保留优先级顺序缩小矿池规模时，不同场景下的性能下降率差异进一步证明，在子链矿池规模受限的情况下，具有较高沙普利值（SV）的矿工将获得更优被选中机会。在实施实验中，PoFLSC共识机制支持子链管理者感知保留优先级及贡献者核心分区，从而建立并维持具有竞争力的子链。