  The valuable data collected by IoT devices in edge networks together with the
resurgence of ML stimulate the latest trend of edge AI. However, recent FL
methods face major challenges including communication bottleneck, data
heterogeneity and security concerns in edge IoT scenarios, especially when
being adopted for distributed learning among massive IoT devices equipped with
limited data and transmission resources. Meanwhile, the swarm nature of IoT
systems is overlooked by most existing literature, which calls for new designs
of distributed learning algorithms. Inspired by the success of biological
intelligence (BI) of gregarious organisms, we propose a novel edge learning
approach for swarm IoT, called communication-efficient and Byzantine-robust
distributed swarm learning (CB-DSL), through a holistic integration of
AI-enabled stochastic gradient descent and BI-enabled particle swarm
optimization. To deal with non-i.i.d. data issues and Byzantine attacks, global
data samples are introduced in CB-DSL and shared among IoT workers, which not
only alleviates the local data heterogeneity effectively but also enables to
fully utilize the exploration-exploitation mechanism of swarm intelligence.
Further, we provide convergence analysis to theoretically demonstrate that the
proposed CB-DSL is superior to the standard FL with better convergence
behavior. In addition, to measure the effectiveness of the introduction of the
globally shared dataset, we also evaluate the model divergence by deriving its
upper bound, which is related to the distance between the data distribution at
local IoT devices and the population distribution for the whole datasets.
Numerical results verify that the proposed CB-DSL outperforms the existing
benchmarks in terms of faster convergence speed, higher convergent accuracy,
lower communication cost, and better robustness against non-i.i.d. data and
Byzantine attacks.
