Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm
<|reference_start|>Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm: 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.<|reference_end|>
@article{sokhankhosh2024proof-of-collaborative-learning:,
    title={Proof-of-Collaborative-Learning: A Multi-winner Federated Learning
  Consensus Algorithm},
    author={Amirreza Sokhankhosh, Sara Rouhani},
    journal={arXiv preprint arXiv:2407.13018},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13018},
    primaryClass={cs.DC cs.LG}
}