Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm
This addresses energy waste in blockchain systems for developers and users, though it appears incremental by adapting federated learning to consensus.
The paper tackles the energy inefficiency of blockchain consensus mechanisms like Proof-of-Work by proposing Proof-of-Collaborative-Learning (PoCL), which redirects computational power to train federated learning models, and demonstrates fair reward distribution across rounds.
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.