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

TL;DR
This paper introduces PoFLSC, a novel blockchain consensus mechanism leveraging federated learning and partner selection based on dataset value, demonstrated through simulations showing improved miner selection fairness.
Contribution
The paper proposes PoFLSC, a new federated learning-based blockchain consensus with a subchain for training and auditing, emphasizing dataset value in partner selection.
Findings
Higher Shapley Value miners have better selection chances.
PoFLSC effectively supports subchain management and contributor partitioning.
Simulation results validate the approach's effectiveness.
Abstract
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…
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Taxonomy
TopicsBlockchain Technology Applications and Security · Privacy-Preserving Technologies in Data · Cryptography and Data Security
MethodsAttentive Walk-Aggregating Graph Neural Network
