Machine learning (ML) typically relies on the assumption that training and testing distributions are identical and that data are centrally stored for training and testing. However, in real-world scenarios, distributions may differ significantly, and data are often distributed across different devices, organizations, or edge nodes. Consequently, it is to develop models capable of effectively generalizing across unseen distributions in data spanning various domains. In response to this challenge, there has been a surge of interest in federated domain generalization (FDG) in recent years. FDG synergizes federated learning (FL) and domain generalization (DG) techniques, facilitating collaborative model development across diverse source domains for effective generalization to unseen domains, all while maintaining data privacy. However, generalizing the federated model under domain shifts remains a complex, underexplored issue. This article provides a comprehensive survey of the latest advancements in this field. Initially, we discuss the development process from traditional ML to domain adaptation (DA) and DG, leading to FDG, as well as provide the corresponding formal definition. Subsequently, we classify recent methodologies into four distinct categories: federated domain alignment (FDAL), data manipulation (DM), learning strategies (LSs), and aggregation optimization (AO), detailing appropriate algorithms for each. We then overview commonly utilized datasets, applications, evaluations, and benchmarks. Conclusively, this survey outlines potential future research directions.

Mobile edge computation (MEC) has recently emerged as a promising computing paradigm for supporting latency-sensitive mobile applications. Due to the limited resources of the edge servers (ESs), efficient resource allocation mechanisms are key to realize the MEC paradigm. In such a resource allocation process, it is a significant challenge to guarantee truthfulness while enabling flexible task offloading and satisfying the locality constraint. To address such a challenge, we propose a truthful auction-based resource allocation mechanism with flexible task offloading (TARFO) in an MEC system. Specifically, we first design the minimum delay task graph partitioning algorithm, aiming at calculating the minimum completion time and the task offloading solutions under different resource profiles. Based on this algorithm, for each smart mobile device (SMD), we further determine the set of feasible non-dominated resource profiles and the corresponding task offloading solutions. We next propose an efficient primal-dual approximation winning bid selection algorithm to determine the set of the winning bids and a critical value based pricing algorithm to calculate the payments of the winning bids. Strict theoretical analysis demonstrates TARFO can ensure truthfulness, individual rationality, computational efficiency and a smaller approximation ratio. Simulation results verify the effectiveness and efficiency of TARFO.

Mobile edge computation (MEC) is envisioned as a prospective approach for processing the computation-intensive and delay-sensitive tasks of smart mobile devices (SMDs) through offloading them to base stations (BSs) nearby. In fact, efficient task offloading mechanisms are crucial to accomplish an MEC system. The key challenge is to make on-spot decisions upon the arrival of each task and at the same time achieve truthfulness of each SMD. The challenge further escalates, when the unique characteristics of an MEC system, such as locality constraint, delay constraint, etc., are explicitly considered. To solve the challenge, we present a truthful online combinatorial auction-based mechanism (TOCA) for task offloading in an MEC system. Specifically, we first devise the candidate offloading scheme determination algorithm, aiming to determine the candidate offloading schemes of an SMD upon the arrival of its task. Next, we devise the winning offloading scheme selection and pricing algorithm based on the online primal-dual optimization framework, to decide the winning scheme among the SMD's candidate offloading schemes and calculate its payment. By solid theoretical analysis, we verify that TOCA achieves truthfulness, individual rationality and computational efficiency and a smaller competitive ratio. Trace-driven simulation studies validate the effectiveness and efficacy of TOCA.

Recently, Edge-Cloud Collaborative (ECC) has emerged as an efficient and promising technique to empower various computation-intensive applications in Digital Twin Network (DTN). The integration of ECC and DTN serves to bridge the gap between data analysis and physical states. In ECC, a reliable and optimal task offloading scheme is required to maximize resource utilization and provide satisfying services to End Users (EU). However, existing offloading schemes still face significant challenges, such as the instability and complexity of network topologies, the intricacies of massive data, and the lack of trust among EU. In this paper, we propose an enhancinG edge-clOud collaboraTion wiTh blockchain-assistEd digital twin intelligence offloadiNg scheme (GOTTEN) which transmits large-scale tasks generated by DTs to Edge Station (ES) or Cloud Station (CS) in dynamic DTN scenarios. We first formulate this resource allocation and task offloading problem and provide an appropriate initial solution which guarantees that tasks generated by DTs can be accurately mapped to physical entities, while optimizing block allocation and reducing the decision space of task offloading. Then, we employ the Lagrange Multiplier based Distributed Island model-enhanced Genetic Algorithm (LM-DIGA) to transform our formulated problem into a convex form and achieve an optimal resource allocation under a specific scheme. Additionally, our proposed architecture also leverages blockchain verification mechanisms to enhance system stability, strengthening privacy protection for DT data as well. Finally, extensive simulation results demonstrate that, compared with seven baselines, our proposed scheme achieves a 10 percent the total system delay and privacy overhead with regard to other schemes in ECC.

A UAV team shows tremendous potential for various mobile scenarios. However, some evidences reveal their vulnerability to False Data Injection (FDI) attacks, which can significantly jeopardize the flight security or even lead to catastrophic incidents. Existing studies primarily focus on detecting or defending against FDI attacks at the trajectory control of individual UAVs, leaving a gap in a comprehensive secure mechanism that can simultaneously detect, localize, and compensate for such attacks across an entire UAV team. The complexity of developing such a solution is magnified by the multiple design goals, the inherent sophistication of UAV team, and practical attack assumptions. In this paper, we propose a joint secure framework based on multi-task deep learning to simultaneously detect FDI attacks, localize the compromised components, and compensate control signals to mitigate the impact of FDI attacks on promising UAV teams. Specifically, we design an all-in-one deep learning model framework with a temporal-spatial information extraction module and a hierarchical multi-task module to perform three tasks simultaneously. Moreover, we introduce an iterative learning method with experience replay to counteract knowledge decay during model training. Extensive experiments and real flight demonstrations are presented to validate the improved performance and the benefits of our proposed secure method.