Consensus mechanisms are the core of any blockchain system. However, the majority of these mechanisms do not target federated learning directly nor do they aid in the aggregation step. This paper introduces Proof of Reasoning (PoR), a novel consensus mechanism specifically designed for federated learning using blockchain, aimed at preserving data privacy, defending against malicious attacks, and enhancing the validation of participating networks. Unlike generic blockchain consensus mechanisms commonly found in the literature, PoR integrates three distinct processes tailored for federated learning. Firstly, a masked autoencoder (MAE) is trained to generate an encoder that functions as a feature map and obfuscates input data, rendering it resistant to human reconstruction and model inversion attacks. Secondly, a downstream classifier is trained at the edge, receiving input from the trained encoder. The downstream network's weights, a single encoded datapoint, the network's output and the ground truth are then added to a block for federated aggregation. Lastly, this data facilitates the aggregation of all participating networks, enabling more complex and verifiable aggregation methods than previously possible. This three-stage process results in more robust networks with significantly reduced computational complexity, maintaining high accuracy by training only the downstream classifier at the edge. PoR scales to large IoT networks with low latency and storage growth, and adapts to evolving data, regulations, and network conditions.

翻译：共识机制是任何区块链系统的核心。然而，大多数现有机制并非直接针对联邦学习设计，也未能在聚合步骤中提供有效支持。本文提出一种新颖的共识机制——推理证明，专为基于区块链的联邦学习设计，旨在保护数据隐私、防御恶意攻击并增强参与网络的验证能力。与文献中常见的通用区块链共识机制不同，推理证明集成了三个为联邦学习定制的独立流程。首先，训练一个掩码自编码器以生成编码器，该编码器作为特征映射器对输入数据进行混淆处理，使其能够抵抗人工重构和模型反演攻击。其次，在边缘端训练下游分类器，接收来自已训练编码器的输入。随后将下游网络的权重、单个编码数据点、网络输出及真实标签添加到区块中，以供联邦聚合使用。最后，这些数据促进了所有参与网络的聚合，使得比以往更复杂且可验证的聚合方法成为可能。这种三阶段流程能够构建更鲁棒的网络，在仅训练边缘端下游分类器的前提下显著降低计算复杂度并保持高精度。推理证明机制可扩展至大规模物联网网络，具有低延迟和存储增长缓慢的特性，并能适应不断变化的数据、法规和网络条件。