Balancing Privacy Protection and Interpretability in Federated Learning

Federated learning (FL) aims to collaboratively train the global model in a distributed manner by sharing the model parameters from local clients to a central server, thereby potentially protecting users' private information. Nevertheless, recent studies have illustrated that FL still suffers from information leakage as adversaries try to recover the training data by analyzing shared parameters from local clients. To deal with this issue, differential privacy (DP) is adopted to add noise to the gradients of local models before aggregation. It, however, results in the poor performance of gradient-based interpretability methods, since some weights capturing the salient region in feature map will be perturbed. To overcome this problem, we propose a simple yet effective adaptive differential privacy (ADP) mechanism that selectively adds noisy perturbations to the gradients of client models in FL. We also theoretically analyze the impact of gradient perturbation on the model interpretability. Finally, extensive experiments on both IID and Non-IID data demonstrate that the proposed ADP can achieve a good trade-off between privacy and interpretability in FL

MSCET : a multi-scenario offloading schedule for biomedical data processing and analysis in cloud-edge-terminal collaborative vehicular networks

With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoTs), an increasing number of computation intensive or delay sensitive biomedical data processing and analysis tasks are produced in vehicles, bringing more and more challenges to the biometric monitoring of drivers. Edge computing is a new paradigm to solve these challenges by offloading tasks from the resource-limited vehicles to Edge Servers (ESs) in Road Side Units (RSUs). However, most of the traditional offloading schedules for vehicular networks concentrate on the edge, while some tasks may be too complex for ESs to process. To this end, we consider a collaborative vehicular network in which the cloud, edge and terminal can cooperate with each other to accomplish the tasks. The vehicles can offload the computation intensive tasks to the cloud to save the resource of edge. We further construct the virtual resource pool which can integrate the resource of multiple ESs since some regions may be covered by multiple RSUs. In this paper, we propose a Multi-Scenario offloading schedule for biomedical data processing and analysis in Cloud-Edge-Terminal collaborative vehicular networks called MSCET. The parameters of the proposed MSCET are optimized to maximize the system utility. We also conduct extensive simulations to evaluate the proposed MSCET and the results illustrate that MSCET outperforms other existing schedules. © 2004-2012 IEEE

Edge data based trailer inception probabilistic matrix factorization for context-aware movie recommendation

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature

PROSTATIC SCHISTOSOMA JAPONICUM WITH ATYPICAL IMMUNOPHENOTYPING OF INDIVIDUAL GLANDULAR TUBES: A CASE REPORT AND REVIEW OF THE LITERATURE

There are few cases of prostatic schistosomiasis. Here we report a case of Schistosoma japonicum of the prostate, in which the immunophenotyping of individual glandular tubes was atypical. Whether the S. japonicum infection contributed to the lesion or not is unknown. We suspect the lesion was a sign of early precancerous hyperplasia. Follow-up of this patient may give clues about the relationship between schistosomiasis and prostate cancer. This is the first case report of prostatic S. japonicum in the English literatures. A review of the literature is carried out

Discovery and Timing of Millisecond Pulsars in the Globular Cluster M5 with FAST and Arecibo

We report on a comprehensive multiwavelength study of the pulsars in the globular cluster (GC) M5, including the discovery of M5G, a new compact noneclipsing “black widow” pulsar. Thanks to the analysis of 34 yr of radio data taken with the Five-hundred-meter Aperture Spherical radio Telescope and Arecibo telescopes, we obtained new phase-connected timing solutions for four pulsars and improved those of the other three. These have resulted in, among other things, (a) much improved proper motions for five pulsars, with transverse velocities (relative to the cluster) that are smaller than their respective escape velocities; (b) 3 σ and 1.5 σ detections of Shapiro delays in M5F and M5D, respectively; and (c) greatly improved measurement of the periastron advance in M5B, whose value of \dot{\omega }=0\buildrel{\circ}\over{.} 01361(6) implies that M5B is still likely to be a heavy ( ${m}_{p}={1.981}_{-0.088}^{+0.038}\,{M}_{\odot }$ ) neutron star. The binary pulsars M5D, M5E, and M5F are confirmed to be in low-eccentricity binary systems, the low-mass companions of which are newly identified to be He white dwarfs using Hubble Space Telescope data. Four pulsars are also found to be associated with X-ray sources. Similarly to the eclipsing pulsar M5C, M5G shows little or no nonthermal X-ray emission, indicative of weak synchrotron radiation produced by intrabinary shocks. All seven pulsars known in M5 have short spin periods (<8 ms), and five are in binary systems with low orbital eccentricities. These characteristics differ from the overall GC pulsar population but confirm the expectations for the pulsar population in a cluster with a small rate of stellar encounters per binary system