Proof-of-Federated-Learning-Subchain: Free Partner Selection Subchain
  Based on Federated Learning
Boyang Li Bingyu Shen Qing Lu Taeho Jung Yiyu Shi
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.