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Federated learning (FL) provides a collaborative framework that enables intelligent networking devices to train a shared model without the need to share local data. FL has been applied in communication networks, which offers the dual advantage of preserving user privacy and reducing communication overhead. Networking systems and FL are highly complementary. Networking environments provide critical support for data acquisition, edge computing capabilities, round communication/connectivity, and scalable topologies. In turn, FL can leverage capabilities to achieve learning adaptation, low-latency operation, edge intelligence, personalization, and, notably, privacy preservation. In our review, we gather relevant literature and open-source platforms that point out the feasibility of conducting experiments at the confluence of FL and intelligent networking. Our review is structured around key sections, including the introduction of FL concepts, the background of FL applied in networking, and experimental simulations covering networking for FL and FL for networking. Additionally, we delved into case studies showcasing FL potential in optimizing state-of-the-art network optimization objectives, such as learning performance, quality of service, energy, and cost. We also addressed the challenges and outlined future research directions that provide valuable guidance to researchers and practitioners in this trending field.

Recent studies have shown that Plasmodium falciparum malaria parasites in Pailin province, along the border between Thailand and Cambodia, have become resistant to artemisinin derivatives. To better define the epidemiology of P. falciparum populations and to assess the risk of the possible spread of these parasites outside Pailin, a new epidemiological tool named "Focused Screening and Treatment" (FSAT), based on active molecular detection of asymptomatic parasite carriers was introduced in 2010. Cross-sectional malariometric surveys using PCR were carried out in 20 out of 109 villages in Pailin province. Individuals detected as P. falciparum carriers were treated with atovaquone-proguanil combination plus a single dose of primaquine if the patient was non-G6PD deficient. Interviews were conducted to elicit history of cross-border travel that might contribute to the spread of artemisinin-resistant parasites. After directly observed treatment, patients were followed up and re-examined on day 7 and day 28. Among 6931 individuals screened, prevalence of P. falciparum carriers was less than 1%, of whom 96% were asymptomatic. Only 1.6% of the individuals had a travel history or plans to go outside Cambodia, with none of those tested being positive for P. falciparum. Retrospective analysis, using 2010 routine surveillance data, showed significant differences in the prevalence of asymptomatic carriers discovered by FSAT between villages classified as "high risk" and "low risk" based on malaria incidence data. All positive individuals treated and followed-up until day 28 were cured. No mutant-type allele related to atovaquone resistance was found. FSAT is a potentially useful tool to detect, treat and track clusters of asymptomatic carriers of P. falciparum along with providing valuable epidemiological information regarding cross-border movements of potential malaria parasite carriers and parasite gene flow.

Intelligent healthcare networks represent a significant component in digital applications, where the requirements hold within quality-of-service (QoS) reliability and safeguarding privacy. This paper addresses these requirements through the integration of enabler paradigms, including federated learning (FL), cloud/edge computing, software-defined/virtualized networking infrastructure, and converged prediction algorithms. The study focuses on achieving reliability and efficiency in real-time prediction models, which depend on the interaction flows and network topology. In response to these challenges, we introduce a modified version of federated logistic regression (FLR) that takes into account convergence latencies and the accuracy of the final FL model within healthcare networks. To establish the FLR framework for mission-critical healthcare applications, we provide a comprehensive workflow in this paper, introducing framework setup, iterative round communications, and model evaluation/deployment. Our optimization process delves into the formulation of loss functions and gradients within the domain of federated optimization, which concludes with the generation of service experience batches for model deployment. To assess the practicality of our approach, we conducted experiments using a hypertension prediction model with data sourced from the 2019 annual dataset (Version 2.0.1) of the Korea Medical Panel Survey. Performance metrics, including end-to-end execution delays, model drop/delivery ratios, and final model accuracies, are captured and compared between the proposed FLR framework and other baseline schemes. Our study offers an FLR framework setup for the enhancement of real-time prediction modeling within intelligent healthcare networks, addressing the critical demands of QoS reliability and privacy preservation. [ABSTRACT FROM AUTHOR]