School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou, China.
Department of Computer Engineering, Hongik University, Seoul, Korea.
PLoS One. 2024 Aug 16;19(8):e0308991. doi: 10.1371/journal.pone.0308991. eCollection 2024.
Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services.
各种深度学习技术，包括基于区块链的方法，已经被探索用于释放边缘数据处理和由此产生的智能的潜力。然而，现有研究往往忽略了区块链共识处理在典型物联网 (IoT) 边缘网络设置中的资源要求。本文提出了我们的 FLCoin 方法。具体来说，我们提出了一种新的基于委员会的共识处理方法，委员会成员通过 FL 过程选举产生。此外，我们还采用了两层区块链架构来进行联邦学习 (FL) 处理，以促进区块链和 FL 技术的无缝集成。我们的分析表明，随着网络规模的增加，通信开销保持稳定，确保了我们基于区块链的 FL 系统的可扩展性。为了评估所提出方法的性能，我们使用 MNIST 数据集进行了实验，以训练一个标准的五层 CNN 模型。我们的评估表明了 FLCoin 的效率。随着参与模型训练的节点数量的增加，共识延迟始终保持在 3 秒以下，从而总训练时间较短。值得注意的是，与使用 PBFT 作为共识协议的基于区块链的 FL 系统相比，我们的方法在通信开销方面提高了 90%，在训练时间成本方面降低了 35%。我们的方法确保了高效和可扩展的解决方案，使区块链和 FL 能够集成到物联网边缘网络中。所提出的架构为构建智能物联网服务提供了坚实的基础。
