NebulaFL: Self-Organizing Efficient Multilayer Federated Learning Framework With Adaptive Load Tuning in Heterogeneous Edge Systems

As a promising edge intelligence technology, federated learning (FL) enables Internet of Things (IoT) devices to train the models collaboratively while ensuring the data privacy and security. Recently, hierarchical FL (HFL) has been designed to promote distributed training in the intricate hierarchical structure of IoT. However, the coarse-grained hierarchical schemes usually fail to thoroughly adapt to the hierarchical environment, leading to high training latency. Meanwhile, highly heterogeneous communication and computation delays due to the device diversity (the system heterogeneity) and decentralized data distribution due to the decentralized device distribution (the data heterogeneity) exacerbate the above challenges. This article proposes NebulaFL, a dual heterogeneity-aware multilayer FL framework, to support efficient distributed training in IoT scenarios. NebulaFL proposes an innovative multilayer architecture organization scheme to adapt the complex hierarchical heterogeneous scenarios. Specifically, through a finer-grained division of the HFL hierarchy, hybrid synchronous-asynchronous training is implemented at both the global system and local device-layer levels. More importantly, to adaptively build a heterogeneity-aware hierarchical training architecture, NebulaFL considers the effect of dual heterogeneity in the architectural organization scheme to determine the optimal location of devices in a multilayer environment. To further improve the training efficiency during the training process, NebulaFL employs an augmented multiarmed bandit technique based on the reinforcement learning to adjust the device-layer training load by evaluating the dynamic training utility and convergence uncertainty feedback. Experiments demonstrate that NebulaFL achieves up to a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$15.68\times $ </tex-math></inline-formula> speed-up ratio and a 23.94% increase in the training accuracy compared to the latest or classic approaches.