On the feasibility of Federated Learning towards on-demand client deployment at the edge

Nowadays, researchers are investing their time and devoting their efforts in developing and motivating the 6G vision and resources that are not available in 5G. Edge computing and autonomous vehicular driving applications are more enhanced under the 6G services that are provided to successfully operate tasks. The huge volume of data resulting from such applications can be a plus in the AI and Machine Learning (ML) world. Traditional ML models are used to train their models on centralized data sets. Lately, data privacy becomes a real aspect to take into consideration while collecting data. For that, Federated Learning (FL) plays nowadays a great role in addressing privacy and technology together by maintaining the ability to learn over decentralized data sets. The training is limited to the user devices only while sharing the locally computed parameter with the server that aggregates those updated weights to optimize a global model. This scenario is repeated multiple rounds for better results and convergence. Most of the literature proposed client selection methods to converge faster and increase accuracy. However, none of them has targeted the ability to deploy and select clients in real-time wherever and whenever needed. In fact, some mobile and vehicular devices are not available to serve as clients in the FL due to the highly dynamic environments and/or do not have the capabilities to accomplish this task. In this paper, we address the aforementioned limitations by introducing an on-demand client deployment in FL offering more volume and heterogeneity of data in the learning process. We make use of containerization technology such as Docker to build efficient environments using any type of client devices serving as volunteering devices, and Kubernetes utility called Kubeadm to monitor the devices. The performed experiments illustrate the relevance of the proposed approach and the efficiency of the deployment of clients whenever and wherever needed

ON-DEMAND-FL: A Dynamic and Efficient Multi-Criteria Federated Learning Client Deployment Scheme

In this paper, we increase the availability and integration of devices in the learning process to enhance the convergence of federated learning (FL) models. To address the issue of having all the data in one location, federated learning, which maintains the ability to learn over decentralized data sets, combines privacy and technology. Until the model converges, the server combines the updated weights obtained from each dataset over a number of rounds. The majority of the literature suggested client selection techniques to accelerate convergence and boost accuracy. However, none of the existing proposals have focused on the flexibility to deploy and select clients as needed, wherever and whenever that may be. Due to the extremely dynamic surroundings, some devices are actually not available to serve as clients in FL, which affects the availability of data for learning and the applicability of the existing solution for client selection. In this paper, we address the aforementioned limitations by introducing an On-Demand-FL, a client deployment approach for FL, offering more volume and heterogeneity of data in the learning process. We make use of the containerization technology such as Docker to build efficient environments using IoT and mobile devices serving as volunteers. Furthermore, Kubernetes is used for orchestration. The Genetic algorithm (GA) is used to solve the multi-objective optimization problem due to its evolutionary strategy. The performed experiments using the Mobile Data Challenge (MDC) dataset and the Localfed framework illustrate the relevance of the proposed approach and the efficiency of the on-the-fly deployment of clients whenever and wherever needed with less discarded rounds and more available data

The Metaverse: Survey, Trends, Novel Pipeline Ecosystem & Future Directions

The Metaverse offers a second world beyond reality, where boundaries are non-existent, and possibilities are endless through engagement and immersive experiences using the virtual reality (VR) technology. Many disciplines can benefit from the advancement of the Metaverse when accurately developed, including the fields of technology, gaming, education, art, and culture. Nevertheless, developing the Metaverse environment to its full potential is an ambiguous task that needs proper guidance and directions. Existing surveys on the Metaverse focus only on a specific aspect and discipline of the Metaverse and lack a holistic view of the entire process. To this end, a more holistic, multi-disciplinary, in-depth, and academic and industry-oriented review is required to provide a thorough study of the Metaverse development pipeline. To address these issues, we present in this survey a novel multi-layered pipeline ecosystem composed of (1) the Metaverse computing, networking, communications and hardware infrastructure, (2) environment digitization, and (3) user interactions. For every layer, we discuss the components that detail the steps of its development. Also, for each of these components, we examine the impact of a set of enabling technologies and empowering domains (e.g., Artificial Intelligence, Security & Privacy, Blockchain, Business, Ethics, and Social) on its advancement. In addition, we explain the importance of these technologies to support decentralization, interoperability, user experiences, interactions, and monetization. Our presented study highlights the existing challenges for each component, followed by research directions and potential solutions. To the best of our knowledge, this survey is the most comprehensive and allows users, scholars, and entrepreneurs to get an in-depth understanding of the Metaverse ecosystem to find their opportunities and potentials for contribution

Patterns of Recurrence Following Inguinal Lymph Node Dissection for Penile Cancer: Optimizing Surveillance Strategies

Purpose: Our primary objective is to detail the incidence, site, and timing of penile squamous cell carcinoma (pSCC) recurrence after inguinal lymph node dissection (ILND). Materials and Methods: We performed a retrospective analysis of 551 patients who underwent ILND for pSCC from 2000 to 2017. The primary outcome was pSCC recurrence after ILND. Recurrences were identified and stratified by site. Timing of recurrence was determined. Multivariable logistic regression analysis determined associations with recurrence. Multivariable Cox regression analysis determined associations with overall survival (OS). Sub-group analysis of the distant recurrences analyzed timing and OS by site of distant recurrence. Results: After ILND pSCC recurred in 176 (31.9%) patients. Median time to recurrence was 10 months for distant recurrences, 12 for inguinal, 10.5 for pelvic, and 44.5 for local. Greater than 95% of distant, inguinal, and pelvic recurrences occurred within 48 months of ILND, versus 127 months for local recurrences. Post-ILND recurrence was associated with pN2 (OR 1.99, 95% CI 1.0-4.1), and pN3 (OR 7.2, 95% CI 4.0-13.7). Patients who had local recurrence had similar OS to those without (HR 1.5, 95% CI 0.6-3.8), and worse OS was identified in patients with inguinal (HR 4.5, 95% CI 2.8-7.1), pelvic (HR 2.6, 95% CI 1.5-4.5), or distant (HR 4.0, 95% CI 2.7-5.8) recurrences. Patients with lung recurrences had worse OS than other sites (HR 2.2, 95% CI 1.1-4.3). Conclusions: Of the patients 31.9% had post-ILND recurrence associated with high pN staging. Greater than 95% of distant, inguinal, and pelvic recurrences occurred within 48 months, suggesting surveillance beyond this is low yield. Local recurrences occurred over a longer timeline, emphasizing necessity of long-term surveillance of the primary site