Federated Learning (FL) offers collaborative machine learning without data exposure, but challenges arise in the mobile edge network (MEC) environment due to limited resources and dynamic conditions. This paper presents a Digital Twin (DT)-assisted FL platform for MEC networks and introduces a novel multi-FL service framework to address resource dynamics and mobile users. We leverage DT models to optimize device scheduling and MEC resource allocation, aiming to maximize utility across FL services. Our work includes heuristic and constant approximation algorithms for offline multi-FL service scenarios and we also investigate an online setting of our solution with dynamic bandwidth and moving client conditions. To adapt to changing network conditions, we utilize historical bandwidth data in DTs and implement a deep reinforcement learning algorithm, Ra_DDPG, for automatic bandwidth allocation. Evaluation results demonstrate a significant 49.8% increase in system utility compared to a benchmark algorithm, showcasing the effectiveness of our approach.

How does cultural diversity influence the performance of semi-virtual teams? We use the prism of esports and insights from virtual identity research and so

The IoT era observes the increasing demand for data to support various applications and services. The Mobile Crowdsensing (MCS) system then emerged. By utilizing the hybrid intelligence of humans and sensors, it is significantly beneficial to keep collecting high‐quality sensing data for all kinds of IoT applications, such as environmental monitoring, intelligent healthcare services, and traffic management. However, the service quality of MCS systems relies on a dedicated designed task allocation framework, which needs to consider the participant resource bottleneck and system utility at the same time. Recent studies tend to use a different solution to solve the two challenges. The incentive mechanism is for resolving the participant shortage problem, and task assignment methods are studied to find the best match of participants and system utility goal of MCS. Thus, existing task allocation frameworks fail to consider the participant’s expectations deeply. We propose a semiopportunistic concept‐based solution to overcome this issue. Similar to the “shared mobility” concept, our proposed task allocation framework can offer the participants routing advice without disturbing their original travel plan. The participant can accomplish the sensing request on his route. We further consider the system constraints to determine a subgroup of participants that can obtain the utility optimization goal. Specifically, we use the Graph Attention Network (GAT) to produce the target sensing area’s virtual representation and provide the participant with a payoff‐maximized route. Such a method makes our solution adapt to most of MCS scenarios’ conditions instead of using fixed system settings. Then, a reinforcement learning‐ (RL‐) based task assignment is adopted, which can help the MCS system towards better performance improvements while support different utility functions. The simulation results on various conditions demonstrate the superior performance of the proposed solution.

Here, a methodology based on cluster analysis, feature selection, and support vector machine (SVM) is presented to predict power quality indexes such as harmonic distortion and voltage deviation. First, cluster analysis is made on power quality index data, and historical data sets to be used are selected according to the classification of the data to be predicted. Then, with the feature selection algorithm, the optimal factor combination relevant to the power quality index to be predicted is obtained from several factors, such as weather, active power, reactive power, and others. Finally, the historical data and associated factor data are taken as the training sample data sets to establish the SVM prediction model. Through the analysis of the actual sample data during 2013 and 2014 in a certain area of Shanghai, China, it is proved that the average relative error of this method is greatly improved.

The rapid development of large-scale, diverse data and machine learning (ML) technologies has facilitated the rise of intelligent applications across various sectors. However, concerns over privacy and security risks associated with data collection and ML model training have prompted the emergence of Federated Learning (FL), a distributed machine learning paradigm that ensures privacy-preserving capabilities through collaborative model training. Initially applied to Google's Gboard, FL has since found widespread adoption in domains such as intelligent transportation, recommendation systems, and healthcare. Despite its success, existing FL models primarily focus on optimizing performance while safeguarding privacy, often overlooking the collaborative group's ability to adapt to environmental changes. Drawing parallels to human societies, which effectively adapt to both individual and collective changes, we propose that FL's adaptation to dynamic environments can enhance its ability to support real-world applications. This paper introduces the concept of Federated Open-World Learning (FOWL), a framework that enables FL to not only respond to changes but learn patterns from them, addressing challenges such as participant variability, multi-task learning, and catastrophic forgetting. We provide an evolving view of FL techniques, discuss the shift towards open-world conditions, and compare existing methods for implementing FOWL. Finally, we highlight a number of key challenges and potential future research directions for advancing federated learning in dynamic, open-world environments.