Trustworthy Blockchain-Assisted Federated Learning: Decentralized Reputation Management and Performance Optimization

Blockchain-assisted federated learning (BFL) can achieve decentralized storage and management of model data without relying on a central server. However, security issues caused by deliberate attacks in distributed systems and efficiency issues induced by heterogeneous computing consumption in resource-limited systems need to be urgently addressed in BFL. To address these issues, we propose a decentralized reputation management (DRM) mechanism for a trustworthy BFL (T-BFL) network, that explores, stores, and utilizes the endogenous reputation of distributed nodes to promote system security and efficiency. The proposed DRM includes three core modules, i.e., decentralized reputation evaluation, reputation-based model aggregation, and reputation-based blockchain consensus. Specifically, in the off-chain phase of T-BFL, the reputation value of each node is evaluated based on model quality, which other peer nodes can verify. This reputation value further determines the weight of global aggregation at each node. In the on-chain phase, the reputation of each node serves as the stake to dynamically adjust its consensus difficulty. Furthermore, we investigate the convergence rate of the T-BFL network under the poisoning attack, and dynamically optimize the energy allocation of local training, consensus, and communications by minimizing the upper bound of the global loss function. Extensive experiments are conducted to evaluate the performance of T-BFL on MNIST, Fashion-MNIST, and Cifar-10 datasets. The experimental results demonstrate that, compared with traditional BFL, T-BFL can achieve up to 56.12% accuracy improvement and $8.6\times $ acceleration for reaching the target learning accuracy under the poisoning attack.

I. Introduction

Nowadays, the distributed Internet of Things (IoT) systems, such as Internet of Vehicles [1], [2], [3] and Industrial IoT [4], [5], [6] have gained extensive attention due to its remarkable ability of data collection and fault tolerance. Augmented with sophisticated artificial intelligence techniques, the distributed IoT systems can support intelligent applications over a spectrum of real-world domains, such as fault detection [7], medical diagnosis [8], and image recognition [9]. As the demand of privacy protection prevails, federated learning (FL), as a promising distributed learning mode [10], [11], can be seamlessly applied to the distributed IoT systems for privacy-preserving and cooperative training.