Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm

TL;DR

This paper introduces Proof-of-Collaborative-Learning (PoCL), a blockchain consensus mechanism that leverages federated learning to reduce energy consumption, ensure model quality, and fairly distribute rewards among miners.

Contribution

It presents a novel federated learning-based consensus algorithm, an evaluation mechanism for model efficiency, and a fair reward distribution system for blockchain miners.

Findings

PoCL reduces energy consumption compared to PoW.

The evaluation mechanism effectively assesses local model quality.

The reward system is proven to be fair across rounds.

Abstract

Regardless of their variations, blockchains require a consensus mechanism to validate transactions, supervise added blocks, maintain network security, synchronize the network state, and distribute incentives. Proof-of-Work (PoW), one of the most influential implementations of consensus mechanisms, consumes an extraordinary amount of energy for a task that lacks direct productive output. In this paper, we propose Proof-of-Collaborative-Learning (PoCL), a multi-winner federated learning validated consensus mechanism that redirects the computation power of blockchains to train federated learning models. In addition, we present a novel evaluation mechanism to ensure the efficiency of the locally trained models of miners. We evaluated the security of our evaluation mechanism by introducing and conducting probable attacks. Moreover, we present a novel reward distribution mechanism to incentivize winning miners fairly, and demonstrate that our reward system is fair both within and across all rounds.