Person re-identification is a hot topic in computer vision, and the loss function plays a vital role in improving the discrimination of the learned features. However, most existing models utilize the hand-crafted loss functions, which are usually sub-optimal and challenging to be designed. In this paper, we propose a novel method, AutoLoss-GMS, to search the better loss function in the space of generalized margin-based softmax loss function for person reidentification automatically. Specifically, the generalized margin-based softmax loss function is first decomposed into two computational graphs and a constant. Then a general searching framework built upon the evolutionary algorithm is proposed to search for the loss function efficiently. The computational graph is constructed with a forward method, which can construct much richer loss function forms than the backward method used in existing works. In addition to the basic in-graph mutation operations, the cross-graph mutation operation is designed to further improve the offspring's diversity. The loss-rejection protocol, equivalence-check strategy and the predictor-based promising-loss chooser are developed to improve the search efficiency. Finally, experimental results demonstrate that the searched loss functions can achieve state-of-the-art performance and be transferable across different models and datasets in person re-identification.

To exploit the vast amount of distributed data across edge devices, Federated Learning (FL) has been proposed to learn a shared model by performing distributed training locally on participating devices and aggregating the local models into a global one. The existing FL algorithms suffer from accuracy loss due to that data samples across all devices are usually not independent and identically distributed (non-i.i.d.). Besides, devices might lose connection during the training process in wireless edge computing. Thus, we advocate one-shot Neural Architecture Search technique as a basis to propose a solution which can deal with non-i.i.d. problem and is robust to the intermittent connection issue. We adopt a large network as the global model which includes all the candidate network architectures. The non-i.i.d. problem is alleviated by two steps: (1) identify and train the candidate networks which are potentially high performance and trained with less bias using a heuristic sampling scheme; (2) search for the final model with the highest accuracy rate from the candidate networks. Experimental results show that the model trained by our proposed method is robust to non-i.i.d. problem and can achieve 84% reduced communication overhead compared with the baselines.