Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.

Hard real-time systems are employed in military, aeronautics, and astronautics fields where deployed systems are susceptible to software faults that can result in functional errors. Thus, there is a need to use fault-tolerant (FT) real-time scheduling. Among the various fault-tolerant real-time scheduling techniques, re-execution has been applied widely to existing real-time systems owing to its simplicity and applicability. However, re-execution requires multiple executions of every task, and some tasks miss their deadlines owing to the prolonged execution time; therefore, it has been found to be suitable for only soft real-time systems. In this paper, we propose an FT policy that can be incorporated into most (if not all) existing real-time scheduling algorithms on multiprocessor systems, which improves the reliability of the target system without a tradeoff against schedulability. As a case study, we apply the FT policy to existing fixed-priority scheduling and earliest deadline zero-laxity scheduling, and we demonstrate that it enhances reliability without schedulability loss.

The interference between software components is increasing in safety-critical domains, such as autonomous driving. Low-criticality (LC) tasks, such as vehicle communication, may control high-criticality (HC) tasks, such as acceleration. In such cases, the LC task should also be considered as an HC task because the HC tasks relies on the LC task. However, the difficulty in guaranteeing these LC tasks is the catastrophic cost of computing resources, the electronic control unit in the domain of vehicles, required for every task. In this paper, we theoretically and practically provide safety-guaranteed and inexpensive scheduling for LC tasks by borrowing the computational power of neighbored systems in distributed systems, obviating the need for additional hardware components. As a result, our approach extended the schedulability of LC tasks without violating the HC tasks. Based on the deadline test, the compatibility of our approach with the task-level MC scheduler was higher than that of the system-level MC scheduler, such that the task-level had all dropped LC tasks recovered while the system-level only had 25.5% recovery. Conversely, from the worst-case measurement of violated HC tasks, the HC tasks were violated by the task-level MC scheduler more often than by the system-level MC scheduler, with 70.3% and 15.4% average response time overhead, respectively. In conclusion, under the condition that the HC task ratio has lower than 47% of the overall task systems at 80% of total utilization, the task-level approach with task migration has extensively higher sustainability on LC tasks.

Early successes in controlling the COVID-19 pandemic have prevented Republic of Korea from implementing a prompt, large-scale vaccine rollout to the public. The influence of traditional media on public opinion remains critical and substantial in Republic of Korea, and there have been heated debates about vaccination in traditional media reports in Korea. Effective and efficient public health communication is integral in managing public health challenges. This study explored media reports on the COVID-19 vaccines during the pandemic in Republic of Korea. 12,399 media news reports from May 2020 to September 2021 were collected. An LDA topic model was applied in order to analyze and compare the topics drawn from each study phase using words from the unstructured text data. Although media reports from before the national vaccination implementation focused on the development and rollout of COVID-19 vaccines, diverse topics were reported without any overlap. After the vaccination rollout, the biggest concern was the side effects of the COVID-19 vaccine. In sum, Republic of Korea’s major media outlets reported on diverse topics rather than generating a common discourse about topics related to COVID-19 vaccination.

The Contention-Free (CF) policy has been extensively researched in the realm of real-time multi-processor scheduling due to its wide applicability and the performance enhancement benefits it provides to existing scheduling algorithms. The CF policy improves the feasibility of executing other real-time tasks by assigning the lowest priority to a task at a moment when it is guaranteed not to miss its deadline during the remaining execution time. Despite its effectiveness, existing studies on the CF policy are largely confined to preemptive scheduling, leaving the efficiency and applicability of limited preemption scheduling unexplored. Limited preemption scheduling permits a job to execute to completion with a limited number of preemptions, setting it apart from preemptive scheduling. This type of scheduling is crucial when preemption or migration overheads are either excessively large or unpredictable. In this paper, we introduce SP-CF, a single preemption scheduling approach that incorporates the CF policy. SP-CF allows a preemption only once during each job’s execution, following a priority demotion under the CF policy. We also propose a new schedulability analysis method for SP-CF to determine whether each task is executed in a timely manner and without missing its deadline. Through simulation experiments, we demonstrate that SP-CF can significantly enhance the schedulability of the traditional rate-monotonic algorithm and the earliest deadline first algorithm.