Jianjun Huang

Jianjun Huang, Lixin Ye, Li Kang

In the Industrial Internet of Things (IoT), a large amount of data will be generated every day. Due to privacy and security issues, it is difficult to collect all these data together to train deep learning models, thus the federated learning, a distributed machine learning paradigm that protects data privacy, has been widely used in IoT. However, in practical federated learning, the data distributions usually have large differences across devices, and the heterogeneity of data will deteriorate the performance of the model. Moreover, federated learning in IoT usually has a large number of devices involved in training, and the limited communication resource of cloud servers become a bottleneck for training. To address the above issues, in this paper, we combine centralized federated learning with decentralized federated learning to design a semi-decentralized cloud-edge-device hierarchical federated learning framework, which can mitigate the impact of data heterogeneity, and can be deployed at lage scale in IoT. To address the effect of data heterogeneity, we use an incremental subgradient optimization algorithm in each ring cluster to improve the generalization ability of the ring cluster models. Our extensive experiments show that our approach can effectively mitigate the impact of data heterogeneity and alleviate the communication bottleneck in cloud servers.

X. Wang, Z. Yang, J. Huang et al.

Space-time adaptive processing (STAP) algorithms with coprime arrays can provide good clutter suppression potential with low cost in airborne radar systems as compared with their uniform linear arrays counterparts. However, the performance of these algorithms is limited by the training samples support in practical applications. To address this issue, a robust two-stage reduced-dimension (RD) sparsity-aware STAP algorithm is proposed in this work. In the first stage, an RD virtual snapshot is constructed using all spatial channels but only $m$ adjacent Doppler channels around the target Doppler frequency to reduce the slow-time dimension of the signal. In the second stage, an RD sparse measurement modeling is formulated based on the constructed RD virtual snapshot, where the sparsity of clutter and the prior knowledge of the clutter ridge are exploited to formulate an RD overcomplete dictionary. Moreover, an orthogonal matching pursuit (OMP)-like method is proposed to recover the clutter subspace. In order to set the stopping parameter of the OMP-like method, a robust clutter rank estimation approach is developed. Compared with recently developed sparsity-aware STAP algorithms, the size of the proposed sparse representation dictionary is much smaller, resulting in low complexity. Simulation results show that the proposed algorithm is robust to prior knowledge errors and can provide good clutter suppression performance in low sample support.