With the rapid development of various fields, the demand for talent in the future society is more comprehensive, and interdisciplinary teaching is an important way to cultivate comprehensive talent. However, the current implementation of interdisciplinary teaching in junior high schools has problems such as difficulties in instructional design for teachers, difficulties in in-depth learning for students, and ambiguous evaluation standards. Incorporating the presentation-assimilation-discussion (PAD) class into interdisciplinary teaching can solve these problems. After analyzing the literature related to the PAD class and interdisciplinary teaching and consulting with 13 experienced junior high school teachers. Construct an interdisciplinary teaching model for junior high school that combines an online learning platform with traditional offline class teaching, and design clear and definite evaluation standards. This provides a reference for the interdisciplinary teaching design of junior high school teachers and promotes the cultivation of comprehensive talents.

Interdisciplinary Teaching Model Design for Junior High School Students Based on Presentation-Assimilation-Discussion Class

—Satellite-ground integrated computation where machine learning models trained on satellites and aggregated on Earth offers novel opportunities for federated learning (FL). While satellites in space provide isolated computing environments, satellite-terrestrial (S-T) communication links are exposed to spoofing and hijacking attacks, making transmitted models vulnerable to poisoning attacks. To address this paradigm-specific threat, we introduce STARFed, a novel framework that enhances robustness of satellite-based FL by leveraging S-T link characteristics during model transmission. It comprises three components: (1) crowdsourcing-based link authentication, (2) hybrid poison model detection based on both S-T link and model characteristics, and (3) reputation-based model filtering against adaptive adversaries. Our link-aware defense is of independent interest and can be combined with various FL robust aggregation schemes. We evaluate the framework’s resilience through comprehensive experiments spanning five dataset-model settings and five attacks, including both model and data poisoning attacks. The framework’s performance is compared with six state-of-the-art robust FL aggregation schemes in scenarios with varying degrees of non-IID data distribution, client dropout, and adversarial participation. STARFed demonstrates robust performance across all test scenarios, standing as the only defense mechanism to maintain effectiveness throughout. In the most favorable case, it achieves an increase in FL accuracy of 15.6% compared to the best link-unaware aggregation scheme, with minimal overhead introduced.

STARFed: Link-Aware Defense Against Poisoning Attacks in Satellite-Terrestrial Federated Learning

This document introduces ADPS (Asynchronous Distributed Publish Subscribe), a PubSub-based communication model tailored for a hierarchical architecture distributed on Edge, Fog, and Cloud layers. The primary challenge addressed by this model is establishing runtime connections between subscribers and data providers, enabling communication across various segments. ADPS is implemented within a Smart Cities healthcare application, monitoring vital statistics via IoT devices. Furthermore, this work will cover the specifications of message formats, topic definitions, message delivery reliability and latency effects, broker configurations, control data exchange among nodes, and the dynamic behaviour of topology due to user movement and structural changes with node additions at the Edge and Fog levels. Test results from the model’s most demanding scenarios showed latency variations of approximately 200ms, 300ms, and 400ms with quality of service levels 0, 1, and 2 from the initial Fog node to the Cloud. 

ADPS: Providing asynchronous PubSub communication in an adaptive and scalable way in Edge-Fog-Cloud architectures for healthcare data traffic

With the aging process, the prevalence of chronic diseases and the incidence of disability among the elderly has also increased rapidly. The disabled elderly who are in bed for a long time need long-term monitoring of physiological parameters because of the serious decline of physical function. At the same time, it is also necessary to monitor the surface contact pressure between patients and nursing beds to prevent pressure ulcers. In traditional systems, physiological and stress monitoring are kept separate and are expensive and difficult to operate. Therefore, this paper proposes a real-time, visualization, and cloud-linked disabled elderly monitoring system based on WiFi technology. It can not only monitor physiological parameters in real-time but also monitor surface contact pressure. This system consists of four levels: (1) The sensor terminal nodes collect electrocardiogram (ECG), heart rate (HR), oxygen saturation (SpO2), and collect interface pressure in real-time. (2) The network transport layer adopts WIFI (IEEE 802.11b) protocol. (3) The application layer designs a visual sever written by Qt software. (4) Cloud layer is deployed in the Ali-Cloud IoT server to connect to other cloud medical services. Six subjects took participated in the test and each subject was tested five times. The experimental results demonstrate that the system has a good performance to acquire data, visualize real-time, to connect clouds with a small delay.

A Real-time, Visualization and Cloud-Linked Health Monitoring System for Disabled Elderly: Elderly Health Monitoring System

Federated learning (FL) is a promising distributed learning approach to enable intelligent Internet of Things (IoT) applications. However, FL is vulnerable to model poisoning attacks in which malicious clients abate the accuracy of the global model by committing crafted local model updates to the server. Existing defense methods either rely on a validation data set or simply remove the detected malicious clients from the subsequent training process to handle attacks from a large number of malicious clients. Thus, the performance of existing methods deteriorates drastically in many scenarios where the data distributions of clients are unpredictable. To address these deficiencies, we propose a framework called FL overwatch (FLOW) to efficiently defend against model poisoning attacks taking advantages of the local model updates in current and historical training iterations. On one hand, FLOW detects malicious clients in each iteration by measuring the cosine distances between the local model updates of clients, such that malicious updates are eliminated from the current aggregation. On the other hand, FLOW gracefully punishes the previously identified malicious clients rather than removes them from the whole training process. As a result, FLOWcan embrace a richer reliable set of local model updates than existing methods in aggregation. Extensive experiments on widely used benchmark data sets show that FLOW can achieve higher success defending ratio and higher accuracy of global models over existing Byzantinerobust FL methods under typical untargeted attacks and targeted attacks. Furthermore, FLOW also shows significant effectiveness in defending against adaptive attacks tailored to FLOW.

FLOW: A Robust Federated Learning Framework to Defend Against Model Poisoning Attacks in IoTs

Software Technology Foundation is an important professional foundation course in engineering colleges. To resolve the problems of the traditional teaching mode, which relies on lectures by teachers and hard to improve students’ learning initiative and cultivate their algorithmic thinking, a novel teaching model called Presentation-Assimilation-Discussion (PAD) Class is applied in this paper. For three successive years, online PAD Class, offline PAD Class, and online and offline blended PAD Class were adopted respectively for the practical exploration of the PAD Class teaching model. After practice, the results of the three PAD Classes were evaluated by questionnaires and examination results, and the data and reasons were analyzed. The experiment results show that the online and offline blended PAD Class model is more effective for this course, and it also has a certain reference for other professional foundation courses to practice PAD Class. 

Practical Exploration of PAD Class Teaching Model in Software Technology Foundation

AttenFlow: Context-Aware Architecture with Consensus-Based Retrieval and Graph Attention for Automated Document Processing

FLGuardian: Defending against Model Poisoning Attacks via Fine-grained Detection in Federated Learning

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