Abstract Vehicular networks increasingly necessitate a robust paradigm that harmonizes high-performance multi-task optimization with stringent data transmission privacy. Nevertheless, current research often struggles to achieve an ideal equilibrium between rigorous security, efficient coordination logic, and the constrained computational capacities of on-board units. To surmount these hurdles, this paper proposes a homomorphic encryption-based privacy preservation for adaptive quantum cross-task optimization (QHE-PSO). The proposed scheme synergistically integrates localized identity authentication, homomorphic fitness appraisal, and a cross-domain strategy migration mechanism. By managing encrypted particle swarms in distinct local regions where particles represent specific strategy configurations, QHE-PSO implements a secure migration protocol to facilitate seamless multi-task coordination. Extensive experimental evaluations confirm that QHE-PSO delivers a superior balance of cryptographic resilience and optimization efficacy compared to state-of-the-art benchmarks.

Emerging efficient deep neural network (DNN) models, such as AdderNet, have shown great promise in significantly improving hardware efficiency compared to traditional convolutional neural networks (CNNs). However, achieving low bitwidth quantization and effective model compression remains a major challenge. To this end, we introduce Nonnegative AdderNet (NNAdderNet), a quantization- and compression-friendly AdderNet variant that enables model compression down to 4 bits or even lower. We begin by proposing an equivalent transformation of the sum-of-absolute-difference (SAD) kernel in AdderNet, which allows for the formulation of nonnegative weights. This transformation effectively eliminates the need for a sign bit, thus saving 1 bit per weight. Next, we propose to exploit the dual-sparsity pattern in the weights of the activation-oriented NN-AdderNet quantized model. This inherent sparsity enhances the lossless compression performance over NN-AdderNet. Experimental results show that NN-AdderNet can achieve an average compressed weight bitwidth down to 4 bits or even lower, while achieving negligible accuracy loss as compared to full-precision AdderNet models. Such benefits are further illustrated with hardware-level energy and latency improvements in designing DNN inference accelerators. Consequently, the NN-AdderNet model exhibits both algorithmic and hardware efficiency, thus making it a promising candidate for resource-limited applications.