RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.Comment: 11 pages, 8 figure

Multicystic Changes of Juvenile Nasopharyngeal Angiofibroma: The First Case Report in the Literature

Multicystic changes of juvenile nasopharyngeal angiofibroma: the first case report in the literature. Otolaryngologists, pathologists, and radiologists had better pay attention to this infrequent incidence

The complete chloroplast genome sequence of Acorus gramineus (Acoraceae)

The complete chloroplast genome sequence of Acorus gramineus was assembled and characterized as a resource for future genetic studies. With a total length of 152,887 bp, the chloroplast genome comprised of a large single-copy (LSC) region of 83,005 bp, a small single-copy (SSC) region of 18,230 bp, and two inverted repeat (IR) regions of 25,826 bp. The overall GC contents of the chloroplast genome were 38.7%. A total of 115 genes were predicted, consisting of 80 protein-coding genes, 31 tRNA genes, and 4 rRNA genes. In these genes, nine genes contained one intron and two genes contained two introns. Phylogenetic analysis confirmed the position of A. gramineus within the monocots