Collaborative perception in autonomous driving significantly enhances the perception capabilities of individual agents. Immutable heterogeneity, where agents have different and fixed perception networks, presents a major challenge due to the semantic gap in exchanged intermediate features without modifying the perception networks. Most existing methods bridge the semantic gap through interpreters. However, they either require training a new interpreter for each new agent type, limiting extensibility, or rely on a two-stage interpretation via an intermediate standardized semantic space, causing cumulative semantic loss. To achieve both extensibility in immutable heterogeneous scenarios and low-loss feature interpretation, we propose PolyInter, a polymorphic feature interpreter. It provides an extension point where new agents integrate by overriding only their specific prompts, which are learnable parameters that guide interpretation, while reusing PolyInter’s remaining parameters. By leveraging polymorphism, our design enables a single interpreter to accommodate diverse agents and interpret their features into the ego agent’s semantic space. Experiments on the OPV2V dataset demonstrate that PolyInter improves collaborative perception precision by up to 11.1% compared to SOTA interpreters, while comparable results can be achieved by training only 1.4% of PolyInter’s parameters when adapting to new agents. Code is available at https://github.com/yuchen-xia/PolyInter.

Anomaly detection in distributed systems faces critical challenges from feature heterogeneity—where incomplete or divergent feature sets across nodes degrade detection relia-bility—and privacy risks under regulations like GDPR. While federated learning (FL) enables collaborative training without raw data sharing, existing solutions fail to address both challenges simultaneously: traditional FL suffers from performance drops under feature-missing scenarios, and differential privacy techniques introduce utility penalties. This paper proposes FedMI, a vertical federated learning framework that achieves provable privacy preservation and robust anomaly detection in feature-heterogeneous environments. FedMI's key innovations include a novel framework for vertical federated learning in anomaly detection for distributed systems that maintains high detection accuracy, mimicking real-world distributed system conditions, and a mutual information-guided training mechanism that quantifies and minimizes privacy leakage during federated updates. Evaluations on healthcare, financial, and industrial sensor datasets demonstrate FedMI's robustness: it achieves performance comparable to centralized methods in F1-score under data-island scenarios while ensuring compliance with privacy constraints. By unifying privacy quantification and robustness to feature heterogeneity, FedMI advances the development of dependable AI-driven monitoring for distributed systems.

Collaborative perception enables connected and autonomous vehicles (CAVs) to overcome individual viewpoint limitations by exchanging perception data, making effective resource allocation crucial for timely transmission. However, existing studies focus on resource allocation within a single collaborative perception group (CPG), limiting their effectiveness in multi-group collaborative perception systems. In such a system, each CPG contributes differently to the overall collaborative perception performance, and it is challenging to evaluate and represent CPG system-level utilities. Meanwhile, competition for shared spectrum resources leads to interference and complicates the joint optimization of collaboration mechanisms and spectrum allocation, which is intensified by their temporal scale misalignment. To address these challenges, we propose a Utility-Aware Hierarchical Reinforcement Learning method (UAHRL) to jointly optimize collaboration mechanisms and spectrum allocation. Specifically, we introduce a hierarchical framework to handle temporally misaligned decisions through joint training. The upper layer optimizes the collaborative relationship and granularity over a longer time scale to enhance system-level collaborative performance, while the lower layer allocates spectrum resources over a shorter time interval to fulfill individual CPG transmission demand and enhance system transmission efficiency. To represent and utilize system-level utility, we leverage a feature-based confidence map to assess CAVs’ perception capability and complementarity. A mixing network in the upper layer further decomposes global performance into individual CPG utilities, enabling utility-aware resource allocation. Simulations show that UAHRL outperforms baseline methods in system-level collaborative perception in multi-group systems.

Vehicle platooning has emerged as a promising solution for efficient traffic management. Multiple platoons traveling in a cooperative way can alleviate congestion and enhance driving safety by information sharing and consensus. To address the data security and privacy concerns, blockchain could be applied to enable secure data sharing and consensus across multiple platoons. However, existing performance of blockchain is insufficient to ensure reliable and efficient data consensus among multiple platoons. First, the hierarchical structure of platoons with different roles of vehicles complicates the trust establishment between platoons, making it challenging to evaluate their trustworthiness and ensure consensus reliability. Additionally, data sharing in vehicle platooning networks demands timely information and efficient consensus-building. To tackle above challenges, we design a role-adaptive trust model for trust evaluation of platoons in consideration of different roles of vehicles within a platoon. Based on the proposed model, we formulate a blockchain consensus optimization problem to facilitate both reliability and efficiency of data consensus among multiple platoons. Leveraging Generative Artificial Intelligence (GenAI) techniques, we then propose the Diffusion Enhanced Soft Actor-Critic (DESAC) by integrating the diffusion model and SAC, to further improve the performance of blockchain consensus. Experiment results demonstrate the effectiveness and efficiency of the proposed consensus optimization approach.

Efficient wireless resource allocation is essential for supporting multi-vehicle cooperation. The service data exchanged among intelligent vehicles is typically diverse, with varying transmission requirements that shift according to applications and traffic conditions, leading to major fluctuation in communication situations. Existing multi-agent reinforcement learning based resource allocation methods are often inefficient in handling such nonstationary communication situations due to their rigid cooperation patterns. To this end, we propose a ROle-TRansformable multi-agent resource allocation method, named ROTR. This method adopts a hierarchical decision-making process, where a high-level agent at a base station (BS) dynamically plans and distributes cooperation roles (CRs) and cooperation behaviors (CBs) in response to fluctuating communication situations. The Low-level agents within the transmitting vehicles (TVs) perform role transformations based on the assigned CRs and subsequently receive behavioral guidance according to CBs, enabling dynamic adjustments in cooperation patterns to adapt to variable communication situations and make resource allocation decisions. Additionally, we introduce a non-BS-assisted mode based on policy distillation, which enables a seamless transition to independent operation without the BS, relying solely on local states to generate CRs and CBs, thereby facilitating global resource cooperation. Extensive simulation experiments demonstrate that the proposed framework optimizes resource efficiency in nonstationary vehicular communications.

In the imminent era of 6G, Quality of Service (QoS) emerges as a pivotal concern in wireless communications. The prescribed transmission rates and vast access demands mandated by 6G standards impose heightened requirements on network throughput and delay. However, the highly dynamic and often bursty nature of application demands presents challenges for routing and congestion control. The backpressure-based joint rate and routing control algorithm adaptively adjusts network traffic to achieve optimal throughput. However, varying traffic conditions hinder the algorithm’s convergence to ideal states. Additionally, relying solely on first-order backlog differences for forwarding can lead to poor convergence and high delays. In this study, we propose a Second-Order Smooth Backpressure (SoSBP) algorithm, leveraging second-order backlog metrics and dual-level queue mapping, to address throughput, delay, and jitter issues in dynamic network environments. We validate the efficacy of this novel backlog metric using Lyapunov optimization techniques. Simulation results demonstrate that our approach significantly reduces end-to-end delay and data jitter while preserving throughput and eliminating routing loops.

Vehicle platooning is a promising traffic model in intelligent transportation systems (ITSs), which can effectively improve resource utilization and reduce traffic congestion. The resource allocation for vehicle-to-everything (V2X) communications that consist of intraplatoon communications and interplatoon communications is crucial for safe operation of multiple vehicular platoons. Considering dynamic coordination pattern of vehicular platoons and layered architecture of vehicle platooning networks, a hierarchical resource decision-making framework is proposed in this article. In the proposed framework, the resource decision-making process is divided into two levels. The high level that generates and distributes coordination meta policy is deployed on base station (BS), and the low level that generates ego resource decisions is deployed in each platoon. To deal with optimization of resource allocation for multiplatoon V2X communications, a hierarchical reinforcement learning method (HierNet) is designed based on the proposed hierarchical decision-making framework. In HierNet, meta policy of the high level can be preserved and needs to be updated only when cooperative conditions of multiple platoons undergo distinct changes. Simulation experiments have demonstrated that our proposed method not only optimizes resource efficiency but also reduces the communication costs for resource decision making of vehicle platooning networks.

To support safety driving and various on-board services, efficient resource allocation is crucial for the promising implement of vehicle platooning in intelligent transportation systems (ITSs). The resource allocation of vehicle-to-everything (V2X) communications for vehicular platoons is studied in this article. First, a multiobjective function is formulated to jointly optimize sub-band and power allocation to satisfy Quality-of- Service (QoS) in vehicular networks. With the advantage of dealing with complex decision-making problems in multiagent systems, distributed multiagent deep reinforcement learning (MADRL) stands out for resource allocation of vehicular networks. However, it faces the challenge of cooperation aging when every agent is only learning from information of others to form a cooperation model in the training process. Considering the random and dynamic combination of vehicles in vehicle platooning, a tactic-interactive MADRL method named as TacNet is then proposed to improve the cooperation efficiency of multiple agents. In TacNet, the tactics of other agents will be encoded and transmitted through interactive communications among agents. In addition, with the development of vehicular edge computing (VEC), digital twin (DT) networks are constructed to assist offloading computation-intensive resource allocation tasks in vehicles to the edge. The superiority of the proposed method is verified through extensive simulation results, which refers to convergence and performance of satisfying diversified QoS requirements compared with state-of-the-art MADRL methods.

Edge network slicing promises better utilization of network resources by dynamically allocating resources on demand. However, addressing the imbalance between slice resources and user demands becomes challenging when complex user behaviors lead to bursty traffic within the edge network. Hence, we propose a comprehensive dynamic slice strategy with two coupled sub-strategies (i) bursty-sensitive slice resource coordination and (ii) proactive demand resource matching to find an optimal balance. For obtaining stable strategies, the edge network with bursty traffic is formulated as a bi-level Lyapunov optimization problem. Then we propose a resource allocation and request redirection (RA-RR) algorithm with polynomial complexity by introducing deep reinforcement learning to guarantee real-time. Specifically, two agents are trained to solve two sub-strategies, and the Lyapunov drift-plus-penalty function is used as the reward to keep queues stable. RA-RR is responsive to fluctuations in demand and realizes an efficient interaction of coupled decision-making. Moreover, a training method based on alternating optimization is designed to ensure convergence of the RA-RR algorithm. Experiments demonstrate that the proposal can maximize network revenue while ensuring the stability of slice services when edge traffic bursts, and has an average improvement of 20.4% compared with comparisons.

Network slicing is rapidly prevailing in edge cloud, which provides computing, network and storage resources for various services. When the multiple service providers (SPs) respond to their tenants in parallel, individual decisions on the dynamic and shared edge cloud may lead to resource conflicts. The resource conflicts problem can be formulated as a multi-objective constrained optimization model; however, it is challenging to solve it due to the complexity of resource interactions caused by co-existing multi-SP policies. Therefore, we propose a CommDRL scheme based on multi-agent deep reinforcement learning (MADRL) and multi-agent communication to tackle the challenge. CommDRL can coordinate network resources between SPs with less overhead. Moreover, we design the neurons hotplugging learning in CommDRL to deal with dynamic edge cloud, which realizes scalability without a high cost of model retraining. Experiments demonstrate that CommDRL can successfully obtain deployment policies and easily adapt to various network scales. It improves the accepted requests by 7.4%, reduces resource conflicts by 14.5%, and shortens the model convergence time by 83.3%.

Distributed edge learning collaborates powerful edge devices to train a shared global model. Since the frequent communication between the server and workers is very expensive, it is desired to accelerate the learning process. The gradient sparsification is an efficient method that only uploads a small subset of gradient elements. However, most existing works neglect the distributed nature of local datasets, and consequently the local gradients uploaded by edge devices cannot follow the global correct optimization direction well, which results in the loss of accuracy. In this paper, we propose a new gradient sparsification with a renovating mechanism, called Global Renovating Stochastic Gradient Descent (GRSGD). GRSGD utilizes the previous-round global gradient to estimate the current global one and renovates the current zero-sparsified local gradients. It mitigates the communication overhead while making the convergence direction of training closer to the global optimization, accelerating the distributed edge learning process. We provide a theoretical convergence guarantee for our algorithm based on the non-convex assumption, which better fits most deep learning problems. With extensive experiments in PyTorch, we show that GRSGD effectively accelerates the learning process with a smaller communication cost and a faster convergence rate on most training tasks. For example, on ImageNet MnasNet, GRSGD cuts down the gradient size from 8.47MB to 2.13MB while achieving 9.6%+ higher accuracy.

Artificial intelligence-empowered smart things (e.g., robots, autonomous vehicles, and unmanned aerial vehicles) have been transforming the world. The “brains” of smart things can be abstracted as the agents or cybertwins residing on end devices and edge servers. The next-generation communication networks (i.e., 6G) will become the nervous system for these agents and natively support multiagent cooperation. By sharing local observations and intentions via communication channels, the agents could better understand the environments and make right decisions. Due to the limited channel bandwidth, the communication is considered as a bottleneck of multiagent cooperation. In this article, we propose a graph convolutional communication method (GraphComm) for multiagent cooperation to relive the bottleneck. Specifically, a variational information bottleneck is used to encode the observations and intentions compactly. Furthermore, a graph information bottleneck with the attention-based neighbor sampling mechanism is utilized to improve the effectiveness and robustness of the multiround communication process. The experimental results show that GraphComm can improve the effectiveness, robustness, and efficiency of communication in multiagent cooperative tasks as compared to baseline methods.

The Internet of things (IoT) is becoming more and more flexible and economical with the advancement in information and communication technologies. However, IoT networks will be ultra-dense with the explosive growth of IoT devices. Network function virtualization (NFV) emerges to provide flexible network frameworks and efficient resource management for the performance of IoT networks. In NFV-enabled IoT infrastructure, service function chain (SFC) is an ordered combination of virtual network functions (VNFs) that are related to each other based on the logic of IoT applications. However, the embedding process of SFC to IoT networks is becoming a big challenge due to the dynamic nature of IoT networks and the abundance of IoT terminals. In this paper, we decompose the complex VNFs into smaller virtual network function components (VNFCs) to make more effective decisions since VNF nodes and IoT network devices are usually heterogeneous. In addition, a deep reinforcement learning (DRL) based scheme with experience replay and target network is proposed as a solution that can efficiently handle complex and dynamic SFC embedding scenarios in IoT. Our simulations consider different types of IoT network topologies. The simulation results present the efficiency of the proposed dynamic SFC embedding scheme.

Cloud-based big data platforms provide physical resources for a variety of applications to analyze all forms of data. For the stream big data analytics, a participated task always needs to scale out resources when its input data increases steeply. Typically, the resource scaling out can be achieved by increasing the parallelism degree of the platform based on the experience. However, the resource scaling-out of each task produces additional cost not only from itself but also from other competitive tasks, which brings about great challenges to ensure the efficient utilization of resources. To solve this problem systematically, we consider the resource scaling-out problem as a non-cooperative game and formulate a total cost model including a risk function and a task execution time function. The total cost of resource scaling-out reflects the influence of topology structure for the benefit of a participated task. Hence, two economic classic tax-based incentive policies: Pivotal Mechanism and Externality Mechanism are applied, to stimulate the participation of tasks. We make simulations in different scenarios including node degree and different characteristics of tasks. The simulations results show that our resource scaling-out mechanism can achieve a better performance close to social optimality.