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In this paper, we investigate how to deploy computational intelligence anddeep learning (DL) in edge-enabled industrial IoT networks. In this system, theIoT devices can collaboratively train a shared model without compromising dataprivacy. However, due to limited resources in the industrial IoT networks,including computational power, bandwidth, and channel state, it is challengingfor many devices to accomplish local training and upload weights to the edgeserver in time. To address this issue, we propose a novel multi-exit-basedfederated edge learning (ME-FEEL) framework, where the deep model can bedivided into several sub-models with different depths and output predictionfrom the exit in the corresponding sub-model. In this way, the devices withinsufficient computational power can choose the earlier exits and avoidtraining the complete model, which can help reduce computational latency andenable devices to participate into aggregation as much as possible within alatency threshold. Moreover, we propose a greedy approach-based exit selectionand bandwidth allocation algorithm to maximize the total number of exits ineach communication round. Simulation experiments are conducted on the classicalFashion-MNIST dataset under a non-independent and identically distributed(non-IID) setting, and it shows that the proposed strategy outperforms theconventional FL. In particular, the proposed ME-FEEL can achieve an accuracygain up to 32.7% in the industrial IoT networks with the severely limitedresources.

Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT