The ubiquity of devices in Internet of Things (IoT) has opened up a large source for IoT data. Machine Learning (ML) models with big IoT data is beneficial to our daily life in monitoring air condition, pollution, climate change, etc. However, centralized conventional ML models rely on all clients’ data at a central server, which seriously threatens user privacy. Federated Learning (FL) emerges as a promising solution aiming to protect user privacy by enabling model training on a large corpus of decentralized data. The recent studies indicate FL suffers from the heterogeneity issue as it treats all clients’ data equally, that is, FL might sacrifice performance of majority of clients to accommodate the performance of minority of clients with low usability data. In order to overcome this issue, a Reinforcement Learning (RL) based intelligent central server with the capability of recognizing heterogeneity is implemented, which can help lead the trend towards better performance for majority of clients. To be specific, an FL central server analyses the benefits of different collaboration by capturing the intricate patterns in heterogeneous clients based on rating feedback and then updates clients’ weights iteratively, until it establishes a coalition of clients with quasi-optimal performance. The experimental results on three real dataset under various heterogeneity levels demonstrate the superior performance of the proposed solution.

Large-scale data centers are major infrastructures in the big data era. Therefore, a stable and optimized architecture is required for data center networks (DCNs) to provide services to the applications. Many studies use software-defined network (SDN)-based multipath TCP (MPTCP) implementation to utilize the entire DCN's performance and achieve good results. However, the deployment cost is high. In SDN-based MPTCP solutions, the flow allocation mechanism leads to a large number of forwarding rules, which may lead to storage consumption. Considering the advantages and limitations of the SDN-based MPTCP solution, we aim to reduce the deployment cost due to the use of an extremely expensive storage resource-ternary content addressable memory (TCAM). We combine MPTCP and segment routing (SR) for traffic management to limit the storage requirements. And to the best of our knowledge, we are among the first to use the collaboration of MPTCP and SR in multi-rooted DCN topologies. To explain how MPTCP and SR work together, we use four-layer DCN architecture for better description, which contains physical topology, SR over the topology, multiple path selection supplied by MPTCP, and traffic scheduling on the selected paths. Finally, we implement the proposed design in a simulated SDN-based DCN environment. The simulation results reveal the great benefits of such a collaborative approach.

Multi-access edge computing (MEC) enables end devices with limited computing power to provide effective solutions while dealing with tasks that are computationally challenging. When each end device in an MEC scenario generates multiple tasks, how to reasonably and effectively schedule these tasks is a large-scale discrete action space problem. In addition, how to exploit the objectively existing spatial structure relationships in the given scenario is also an important factor to be considered in task-scheduling algorithms. In this work, we consider indivisible, time-sensitive tasks under this scenario and formalize the task-scheduling problem to minimize the long-term losses. We propose a multiagent collaborative deep reinforcement learning (DRL)-based distributed scheduling algorithm based on graph attention neural networks (GATs) to solve task-scheduling problems in the MEC scenario. Each end device creates a graph representation agent to extract potential spatial features in the scenario and a scheduling agent to extract the timing-related features of the tasks and make scheduling decisions using a gated recurrent unit (GRU). The simulation results show that, compared with several baseline algorithms, our proposed algorithm can take advantage of the spatial positional relationship of devices in the environment, significantly reduce the average delay and drop rate, and improve link utilization.

Today's business environment is characterized by uncertainty and competition, so the capability to adapt to the evolving era and unforeseen challenges is essential in business strategies. Recent studies on extended enterprise indicate that collaboration among different stakeholders is beneficial for surviving these unexpected changes. However, the barriers such as market uncertainty, privacy and trust concerns, and individual contribution evaluation limit the implementation and application of the extended enterprise concept. Federated learning (FL), in which multiple enterprise entities can use a shared model while retaining all training data locally, has emerged as a promising artificial intelligence (AI) solution for accumulating insights from multiple stakeholders and providing collaborative decision-making. Furthermore, the enhanced privacy-protection benefits of FL remove the barriers to implementing extended enterprise collaboration. In particular, an FL central server manages the local updates of multiple enterprise entities (FL clients) and aggregates their contributions to improve the global model training. Meanwhile, to address the time-series graph learning problem in most business environments, we incorporate temporal convolutional network, graph convolutional neural network, and gated recurrent unit architecture into FL to capture the temporal-spatial dependencies in individual data sources. Furthermore, we use traffic flow forecasting as the use case of our proposed framework to verify its effectiveness. Finally, the experimental results on a real traffic flow dataset and the comparison results with the state-of-the-art baseline methods show that our proposed solution achieves superior performance.

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.