In Wireless Sensor Networks (WSNs), relay sensor nodes can aggregate data from edge sensor node into a summary information before sending to the sink. Due to the vast number of sensor nodes in a distributed edge computing (DEC) network, these relay sensor nodes may receive a high number of aggregation requests. This increases the chance of conflicting transmissions, which further leads to unwanted latency. Designing a conflict-free and minimal latency data aggregation schedule remains an open question. Moreover, existing related works have been conducted in traditional WSNs. By leveraging multiple antennas, the Multiple Input Multiple Output (MIMO) and cooperative MIMO called virtual MIMO (V-MIMO) enable broadband wireless communication, thereby improving the performance of WSNs. However, compared with traditional WSNs, MIMO and V-MIMO introduce distinct interference models requiring careful consideration. The work proposes a solution to an NP-hard problem, addressing three challenges: (i) interference; (ii) latency; and (iii) dynamic changes in network topology. Firstly, to counter interference, we propose a model where multiple nodes can simultaneously send data to the same parent by connecting different antennas. Secondly, to minimize latency, we propose a novel distributed heuristic data aggregation scheduling method, which intertwines the construction of an optimal data aggregation tree and conflict-free scheduling. Finally, to handle dynamic network topology changes, we propose lightweight adaptive strategies that do not increase data aggregation latency. Simulation results and theoretical analysis demonstrate superior performance in reducing data aggregation latency. When compared with state-of-the-art solutions, our proposed method decreases data aggregation latency by at least 2.6× on average.

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.

Federated learning stands out as a promising approach within the domain of edge computing, providing a framework for collaborative training on distributed datasets without necessitating data sharing. However, federated learning involves the frequent transmission of machine learning model updates between the server and clients, resulting in high communication costs. Additionally, heterogeneous clients can further complicate the Federated Learning process and deteriorate performance. To address these challenges, we propose Adaptive Self-Knowledge Distillation-based Quality- and Reputation-Aware Cross-Device Federated Learning (ASDQR) - an efficient communication and inference framework designed for heterogeneous clients. ASDQR initiates the process by selecting high-reputation and high-quality clients to be involved in federated learning, significantly impacting communication efficiency and inference effectiveness. ASDQR also introduces a model of adaptive local self-knowledge distillation that incorporates multiple local personalized historical knowledge for more accurate inference, allowing the historical level to be dynamically adjusted across time. Finally, we present an inference-effective aggregation scheme that assigns higher weights to important and reliable local model updates based on clients’ contribution degrees when performing global model aggregation. ASDQR consistently outperforms baseline methods across all datasets and communication rounds, achieving 9.0% higher accuracy than FedAvg, 6.59% higher than MOON, 0.29% higher than FedProx, 0.2% higher than PFedSD, and 0.08% higher than FedMD on the MNIST dataset at 100 communication rounds. Similar improvements are observed on CIFAR, HAR, and WISDM datasets, demonstrating the robustness and efficiency of ASDQR in federated learning with non-IID data.

Cross-silo federated learning (FL) is a privacy-preserving distributed machine learning where organizations acting as clients cooperatively train a global model without uploading their raw local data. Recently, the cross-silo FL in multiaccess edge computing (MEC) is used in increasing industrial applications. Most existing research on cross-silo FL pays attention to the performance aspect, ignoring the incentive mechanism for high-quality client selection and long participation in model training for efficient and stable FL, which has prevented the widespread adoption of cross-silo FL in MEC. In this article, we propose an incentive mechanism with quality-Aware and reputation-Aware based on the infinitely repeated game for cross-silo FL named VARF. VARF selects high-quality and high-reputation edge nodes (ENs) as candidates for model training in the cross-silo FL by a heuristic algorithm and then motivates the selected ENs to actively contribute their resources. VARF also models the long-term behavior of ENs in cross-silo FL as an infinitely repeated game and derives a stable and long-term cooperative strategy for clients while maximizing the amount of local data for model learning in cross-silo FL. Extensive simulations with real-world data sets demonstrate that the performance of VARF is more beneficial than other benchmarks. Meanwhile, experimental results show that cloud platforms (CPs) and ENs eventually form a long and stable cooperative relationship under the trigger strategy.

Recently, most of the Internet of things (IoT) infrastructures are highly centralized with single points of failure, which results in serious security and privacy issues of IoT data. Fortunately, blockchain technique can provide a decentralized and secure IoT framework to deal with security issues based on the characteristics of decentralization, non-tampering, openness, transparency, and traceability. However, the blockchain consensus protocol guarantees the safety and reliability of data, but it also brings problems such as scalability limitations and poor storage extensibility, resulting in the inability to directly integrate blockchain and the IoT in existing conditions. In this article, a private three-tier local blockchain, Three-tier architecture Blockchain (TBchain), is proposed to solve the problem by splitting part of the transactions in the public blockchain and locking them in a higher-level blockchain TBchain. Additionally, the private blockchain TBchain is connected to the public blockchain to build a hierarchical blockchain network to provide privacy protection for the IoT data stored on the blockchain. Finally, we implement an IoT framework based on TBchain and the InterPlanetary File System (IPFS) to realize the decentralized IoT, which guarantees the user’s access control right to personal data. Experimental results show that the IoT framework based on TBchain and IPFS realizes the user’s access control right to personal data by verifying in advance to ensure the confidentiality and security of shared data, and improves the security and privacy of IoT data and transactions. Moreover, we prove that the scalability and storage extensibility of the blockchain is positively correlated with the number of data blocks in TBchain.