Proof of Reasoning for Privacy Enhanced Federated Blockchain Learning at the Edge

TL;DR

This paper introduces Proof of Reasoning, a novel blockchain consensus mechanism designed for federated learning that enhances privacy, security, and validation through a three-stage process involving masked autoencoders and edge training.

Contribution

It presents a new consensus mechanism, Proof of Reasoning, tailored for federated learning in blockchain, integrating privacy-preserving encoding and scalable aggregation methods.

Findings

Reduces computational complexity while maintaining high accuracy.

Enables scalable, low-latency federated learning for IoT networks.

Provides robust validation and security against attacks.

Abstract

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.