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.

我们在这篇论文中提出了一种名为 Proof-of-Federated-Learning-Subchain (PoFLSC) 的新型共识机制，该机制利用子链记录了训练、挑战和审计活动，并强调了合作伙伴选择中宝贵数据集的重要性。通过模拟实验，我们证明了 PoFLSC 的有效性，并展示了当子链池大小有限时，根据 Shapley Value (SV) 的高低选择矿工将获得更好的机会。在进行的实验中，PoFLSC 共识机制支持子链管理者了解预约优先级和贡献者的核心分区，以建立和维护具有竞争力的子链。