A Blockchain-empowered Multi-Aggregator Federated Learning Architecture
  in Edge Computing with Deep Reinforcement Learning Optimization
Xiao Li Weili Wu
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.