In the Internet of Vehicles (IoV), collaborative inference based on Deep Neural Networks (DNNs) faces considerable challenges due to the dynamic and heterogeneous service demands of multiple vehicular devices supported by edge servers. These challenges give rise to a complex joint optimization problem that simultaneously determines the optimal DNN partition points and allocates computing resources from servers to individual devices. This problem is formulated as a Mixed Integer Nonlinear Programming (MINLP) model, involving both discrete partitioning decisions and continuous resource allocation variables, which makes it computationally demanding in large-scale and dynamic environments. To address this issue, we propose a fully distributed algorithm that achieves rapid convergence to feasible solutions. Furthermore, to accelerate the distributed iterative solving process, an adaptive strategy is designed to dynamically adjust partition points and resource allocations according to the real-time server load. For Directed Acyclic Graph (DAG)-structured DNNs, a novel structural transformation technique is developed to convert them into equivalent chain representations, enabling unified optimization across heterogeneous DNN topologies. Theoretical analysis validates the feasibility and near-optimality of the proposed approach under standard consensus assumptions, while empirical results demonstrate stable performance in dynamic vehicular environments. Extensive experiments conducted in realistic vehicular scenarios demonstrate the superior effectiveness and scalability of the proposed method in reducing inference latency and system overhead.

Organizations around the world are increasingly using multiple clouds for critical workloads, which makes the accurate prediction of cloud workload a collaborative task across multiple cloud providers. Our experimental studies on four large-scale cloud workload datasets demonstrate that the workloads are highly heterogeneous, and different cloud providers have highly heterogeneous model preferences, which makes the above task challenging. Accordingly, we propose a joint privacy-preserving cloud workload prediction framework based on Federated Learning (FL), which enables the collaborative training of a workload prediction model across cloud providers with heterogeneous workloads and model preferences without exposing private workload data. To handle workload heterogeneity without privacy leakage, we first design a Generative Adversarial Network (GAN) based virtual workload dataset generation method that incorporates workloads’ temporal and resource-usage-plan-related features via temporal-aware feature calibration. Then, to handle model heterogeneity, we design a FL approach based on a hybrid local model composed of a homogeneous public model and a heterogeneous personal model. Finally, we design a Knowledge Distillation (KD) based approach to post-train the personal model with both private workload knowledge and shared workload knowledge learned from the trained global public model. Extensive experiments on various real-world workloads and actual implementations demonstrate that compared with the state-of-the-art, our framework improves workload prediction accuracy by 57.7% in average over all cloud providers.