Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computational resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.

In response to the growing emphasis on sustainability in federated learning (FL), this research introduces a dynamic, dual-objective optimization framework called Carbon-Conscious Federated Reinforcement Learning (CCFRL). By leveraging Reinforcement Learning (RL), CCFRL continuously adapts client allocation and resource usage in real-time, optimizing both carbon efficiency and model performance. Unlike static or greedy methods that prioritize short-term carbon constraints, existing approaches often suffer from either degrading model performance by excluding high-quality, energy-intensive clients or failing to adequately balance carbon emissions with long-term efficiency. CCFRL addresses these limitations by taking a more sustainable method, balancing immediate resource needs with long-term sustainability, and ensuring that energy consumption and carbon emissions are minimized without compromising model quality, even with non-IID (non-independent and identically distributed) and large-scale datasets. We overcome the shortcomings of existing methods by integrating advanced state representations, adaptive exploration and exploitation transitions, and stagnating detection using t-tests to better manage real-world data heterogeneity and complex, non-linear datasets. Extensive experiments demonstrate that CCFRL significantly reduces both energy consumption and carbon emissions while maintaining or enhancing performance. With up to a 61.78% improvement in energy conservation and a 64.23% reduction in carbon emissions, CCFRL proves the viability of aligning resource management with sustainability goals, paving the way for a more environmentally responsible future in cloud computing.

In order to improve network and mobility management in wireless mobile networks, decoupled control and data planes have been designed, called the Software-Defined Wireless Network (SDWN). In this work, we choose the Locator/Identifier Separation Protocol (LISP) to foster mobility management in an SDWN due to its address scheme. However, LISP adopts a distributed system to manage the location and identifier information of mobile nodes, which does not fit the SDWN requirement of a centralized management entity. Accordingly, recent LISP-related works have focused on how to build a LISP centralized management entity, which does not guarantee routing scalability due to the lack of connectivity with standard LISP sites outside of an SDWN. We present an OpenFlow-based centralized LISP management system where the routing coverage is scalable by the OpenFlow technology, which enables a transparent connectivity between the LISP-SDWN Controller and Mapping System without any changes to the LISP procedure. Another aim of our study is reducing the deployment cost from the perspective of the SDWN provider. The benefits of OpenFlow technology include the fact that LISP-SDWN does not require extra control messages or LISP specification modification, while previous LISP-enabled solutions required extra control messages and procedure changes. Therefore, the deployment cost will be minimized as compared to other LISP-centralized management systems. We evaluate the location management and total operation costs of LISP-SDWN, LISP, the LISP Controller, HMIP, and MIP. The proposed LISP-SDWN shows the lowest management cost in different scale communications such as intra-domain communication, inter-domain communication, and inter-domain with a remote moving node.

In the age of data and machine learning, massive amounts of data produced throughout our society can be rapidly delivered to various applications through a broad spectrum of cloud services. However, the spectrum of applications has vastly different data quality requirements and Willingness-To-Pay(WTP), creating a general and complex problem matching consumer quality requirements and budgets with providers’ data quality and price. This paper proposes the Information-Centric Networking(ICN)-based data marketplace to foster quality-data trading service to address the challenge above. We embed a WTP mechanism into an ICN-based data broker service running on cloud computing; therefore, a data consumer can request its desired data with a data name and quality requirement. By specifying nominal WTPs, data consumers can acquire data of the desired quality at the range of maximum nominal WTP. At the same time, a data broker can offer data of a suitable quality based on the profit-optimized price and the proposed service quality using ground-truth accuracy trained by data. We demonstrate that the data broker’s profit can be almost doubled by using the optimal data size and budget determined by considering the one-leader-multiple-followers Stackelberg game. These results show that a value-added data brokering service can profitably facilitate data trading.

Diagnosing COVID-19 infection through the classification of chest images using machine learning techniques faces many controversial problems owing to the intrinsic nature of medical image data and classification architectures. The detection of lesions caused by COVID-19 in the human lung with properties such as location, size, and distribution is more practical and meaningful to medical workers for severity assessment, progress monitoring, and treatment, thus improving patients’ recovery. We proposed a COVID-19-associated lung lesion detector based on an object detection architecture. It correctly learns disease-relevant features by focusing on lung lesion annotation data of medical images. An annotated COVID-19 image dataset is currently nonexistent. We designed our semi-self-supervised method, which can extract knowledge from available annotated pneumonia image data and guide a novice in annotating lesions on COVID-19 images in the absence of a medical specialist. We prepared a sufficient dataset with nearly 8000 lung lesion annotations to train our deep learning model. We comprehensively evaluated our model on a test dataset with nearly 1500 annotations. The results demonstrated that the COVID-19 images annotated by our method significantly enhanced the model’s accuracy by as much as 1.68 times, and our model competes with commercialized solutions. Finally, all experimental data from multiple sources with different annotation data formats are standardized into a unified COCO format and publicly available to the research community to accelerate research on the detection of COVID-19 using deep learning.