Federated learning (FL) is emerging as a sought-after distributed machine learning architecture, offering the advantage of model training without direct exposure of raw data. With advancements in network infrastructure, FL has been seamlessly integrated into edge computing. However, the limited resources on edge devices introduce security vulnerabilities to FL in the context. While blockchain technology promises to bolster security, practical deployment on resource-constrained edge devices remains a challenge. Moreover, the exploration of FL with multiple aggregators in edge computing is still new in the literature. Addressing these gaps, we introduce the Blockchain-empowered Heterogeneous Multi-Aggregator Federated Learning Architecture (BMA-FL). We design a novel light-weight Byzantine consensus mechanism, namely PBCM, to enable secure and fast model aggregation and synchronization in BMA-FL. We also dive into the heterogeneity problem in BMA-FL that the aggregators are associated with varied number of connected trainers with Non-IID data distributions and diverse training speed. We proposed a multi-agent deep reinforcement learning algorithm to help aggregators decide the best training strategies. The experiments on real-word datasets demonstrate the efficiency of BMA-FL to achieve better models faster than baselines, showing the efficacy of PBCM and proposed deep reinforcement learning algorithm.

翻译：联邦学习（FL）正成为一种备受关注的分布式机器学习架构，其优势在于无需直接暴露原始数据即可进行模型训练。随着网络基础设施的进步，FL已无缝集成到边缘计算中。然而，边缘设备有限的计算资源给该场景下的FL带来了安全漏洞。尽管区块链技术有望增强安全性，但在资源受限的边缘设备上实现实际部署仍是一项挑战。此外，在边缘计算中探索具有多个聚合器的FL在文献中仍属新兴领域。针对这些空白，我们提出了一种区块链赋能的异构多聚合器联邦学习架构（BMA-FL）。我们设计了一种新颖的轻量级拜占庭共识机制PBCM，以实现BMA-FL中安全且快速的模型聚合与同步。我们还深入研究了BMA-FL中的异构性问题：聚合器连接的不同数量的训练器具有非独立同分布的数据分布和多样化的训练速度。我们提出了一种多智能体深度强化学习算法，以帮助聚合器决定最佳训练策略。在真实数据集上的实验表明，BMA-FL能够比基线方法更快地获得更优模型，验证了PBCM和所提深度强化学习算法的有效性。