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

• URL: http://arxiv.org/abs/2407.13018v2
• Date: Wed, 24 Jul 2024 17:04:35 GMT
• Title: Proof-of-Collaborative-Learning: A Multi-winner Federated Learning Consensus Algorithm
• Authors: Amirreza Sokhankhosh, Sara Rouhani,
• Abstract summary: We propose Proof-of-Collaborative-Learning (PoCL), a multi-winner federated learning validated consensus mechanism. PoCL redirects the power of blockchains to train federated learning models. We present a novel evaluation mechanism to ensure the efficiency of the locally trained models of miners.
• Score: 2.5203968759841158
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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