Voting Consensus-Based Decentralized Federated Learning

With the fourth industrial revolution, the construction of the Internet of Things (IoT) has developed vigorously, and machine learning is also widely used in IoT management and data processing. Given the existence of massive distributed and private data sets generated by a large number of IoT devices, centralized machine learning is unsatisfactory. Therefore, federated learning (FL), as a distributed learning method, becomes a promising solution. In FL, clients can train models by transferring model parameters to the aggregation server while keeping private data locally. However, FL still relies on a central server, which has questionable reliability. The single point of failure and limited communication resources also hinder the application of FL in the IoT. In this article, we propose a voting consensus-based decentralized FL (VCDFL) method by incorporating the leader–candidate–follower hierarchical management method and the consensus-based leader election mechanism to solve the single point of failure and exclude outlier models for accelerating convergence during aggregation. Then, we propose a joint decision method to exchange decision information rather than model transfer between clients to further protect privacy and reduce communication overhead while ensuring accuracy. Furthermore, we mathematically derive the probability of successfully electing a leader, the communication efficiency and the joint decision accuracy. We conduct our method in an image recognition scenario. The results show that our joint decision mechanism promotes the accuracy of both system and local decision-making. Meanwhile, the proposed scheme greatly reduces communication costs compared to benchmark learning methods.