With the rapid advances in Large Language Models (LLMs), LLM inference has emerged as a critical research domain. Edge cloud systems have become the preferred platforms for deploying models due to their proximity to end users. Meanwhile, an LLM inference request typically requires multiple and interdependent models, and is also time-sensitive. Thus, how to ensure the timeliness of inference across interdependent models deployed on distributed edge cloud systems remains highly challenging. We quantify the system timeliness of model inference by a metric Age of Model Inference (AoMI). By thorough investigation, we consider that there are two challenges in optimizing this metric, including a lack of model deployment adaptability and accurate model invocation decisions. In this work, we propose a targeting AoMI optimization for joint model inference in edge cloud systems, namely TurboInfer. To tackle the challenge of lacking deployment adaptability under dynamic, highly coupled request-model relationships, we model the request-model interactions as a hypergraph and apply hypergraph partitioning with a two-stage model redeployment mechanism to balance communication and migration costs. To address the challenge of inaccurate model invocation decisions due to complex model dependencies, we introduce a memory association learning method that enhances sample efficiency. The evaluations using real-world traces demonstrate that our method improves the timeliness of LLM inference.
Advanced Persistent Threats (APTs) span multiple stages, hosts, and time windows, making end-to-end attack reconstruction from isolated audit events challenging. Provenance graph construction models causal dependencies among system entities; however, in distributed environments, naive event linking produces dense graphs, and centralized analysis scales poorly over large audit logs. This paper presents a multi-LLM framework for provenance graph construction over distributed audit logs. It combines role-specialized agents with Tool-Mediated Context Reduction (TMCR), enabling each agent to access only task-relevant evidence instead of large raw log streams. The framework further incorporates semantic-aware edge selection and anchor-based global correlation to suppress redundant relations and reconstruct cross-host attack paths. Experiments on the APT29 audit-log scenario show that the proposed framework improves edge precision and attack-path reconstruction while significantly reducing graph complexity and token usage compared to single-LLM and multi-LLM baselines without TMCR.
The quantum Internet aims to distribute entanglement among geographically separated quantum nodes, enabling applications such as quantum key distribution, distributed quantum computing, and cooperative quantum sensing. Since entanglement generation is probabilistic and quantum memory and concurrency are limited, efficient Entanglement Access Control (EAC) is a key challenge. While a purely quantum Medium Access Control (MAC) scheme using multipartite Dicke states has been proposed to resolve contention without classical round-trip communication, prior work mainly focuses on a single Quantum Local Area Network (QLAN). In wide-area quantum networks composed of multiple interconnected QLANs, supporting hierarchical decision making, dynamic winner counts, and fairness under heterogeneous QLAN sizes remains difficult. In this background, we propose Dynamic Hierarchical Entanglement Access Control (DH-EAC), a two-stage quantum lottery scheme for wide-area networks. In the outer stage, a Global Orchestrator uses a Dicke state to select winning QLANs and determine their quotas.
The rapid deployment of foundation models and AI workloads has significantly increased the energy demand of modern data centers, particularly under edge computing environments characterized by limited resources, heterogeneous devices, and intermittent connectivity. This paper proposes a novel federated digital twin-driven framework for energy-aware scheduling and resilience enhancement in edge data centers. The proposed system integrates digital twin modeling, federated learning, and distributed energy-aware optimization to enable privacy-preserving, scalable, and adaptive model training across multiple edge nodes. To address challenges in communication efficiency, heterogeneity, and system robustness, we introduce a Double Robust Federated Learning (DRFL) architecture that incorporates blockchain-based trust management and distributionaware node selection. Experimental analysis demonstrates that the proposed framework improves energy efficiency, scheduling stability, and system resilience while preserving data privacy. This work highlights a cross-layer design paradigm for federated foundation models operating under resource-constrained edge environments.
