Intelligent Client Selection for Federated Learning using Cellular Automata

Federated Learning (FL) has emerged as a promising solution for privacy-enhancement and latency minimization in various real-world applications, such as transportation, communications, and healthcare. FL endeavors to bring Machine Learning (ML) down to the edge by harnessing data from million of devices and IoT sensors, thus enabling rapid responses to dynamic environments and yielding highly personalized results. However, the increased amount of sensors across diverse applications poses challenges in terms of communication and resource allocation, hindering the participation of all devices in the federated process and prompting the need for effective FL client selection. To address this issue, we propose Cellular Automaton-based Client Selection (CA-CS), a novel client selection algorithm, which leverages Cellular Automata (CA) as models to effectively capture spatio-temporal changes in a fast-evolving environment. CA-CS considers the computational resources and communication capacity of each participating client, while also accounting for inter-client interactions between neighbors during the client selection process, enabling intelligent client selection for online FL processes on data streams that closely resemble real-world scenarios. In this paper, we present a thorough evaluation of the proposed CA-CS algorithm using MNIST and CIFAR-10 datasets, while making a direct comparison against a uniformly random client selection scheme. Our results demonstrate that CA-CS achieves comparable accuracy to the random selection approach, while effectively avoiding high-latency clients.Comment: 18th IEEE International Workshop on Cellular Nanoscale Networks and their Application

Intensive-Dose Tinzaparin in Hospitalized COVID-19 Patients: The INTERACT Study

(1) Background: It is well-established that coronavirus disease-2019 (COVID-19) is highly pro-inflammatory, leading to activation of the coagulation cascade. COVID-19-induced hypercoagulability is associated with adverse outcomes and mortality. Current guidelines recommend that hospitalized COVID-19 patients should receive pharmacological prophylaxis against venous thromboembolism (VTE). (2) INTERACT is a retrospective, phase IV, observational cohort study aiming to evaluate the overall clinical effectiveness and safety of a higher than conventionally used prophylactic dose of anticoagulation with tinzaparin administered for VTE prevention in non-critically ill COVID-19 patients with moderate disease severity. (3) Results: A total of 705 patients from 13 hospitals in Greece participated in the study (55% men, median age 62 years). Anticoagulation with tinzaparin was initiated immediately after admission. A full therapeutic dose was received by 36.3% of the participants (mean ± SD 166 ± 33 IU/Kgr/day) and the remaining patients (63.9%) received an intermediate dose (mean ± SD 114 ± 22 IU/Kgr/day). The median treatment duration was 13 days (Q1–Q3: 8–20 days). During the study (April 2020 to November 2021), 14 thrombotic events (2.0%) were diagnosed (i.e., three cases of pulmonary embolism (PE) and 11 cases of deep venous thrombosis, DVT). Four bleeding events were recorded (0.6%). In-hospital death occurred in 12 patients (1.7%). Thrombosis was associated with increasing age (median: 74.5 years, Q1–Q3: 62–79, for patients with thrombosis vs. 61.9 years, Q1–Q3: 49–72, p = 0.0149), increased D-dimer levels for all three evaluation time points (at admission: 2490, Q1–Q3: 1580–6480 vs. 700, Q1–Q3: 400–1475, p p p < 0.0001). Clinical and laboratory improvement was affirmed by decreasing D-dimer and CRP levels, increasing platelet numbers and oxygen saturation measurements, and a drop in the World Health Organization (WHO) progression scale. (4) Conclusions: The findings of our study are in favor of prophylactic anticoagulation with an intermediate to full therapeutic dose of tinzaparin among non-critically ill patients hospitalized with COVID-19