Driven by emerging distributed computing and cloud-edge collaborative applications, metro and regional networks have experienced continuous surges in hub-and-spoke (H&S) traffic, posing great challenges to existing point-to-point network infrastructures. While coherent point-to-multipoint optical transceivers (P2MP-TRXs) present a more cost-effective solution for accommodating H&S traffic, existing P2MP networking paradigms fail to address the dynamic and asymmetric nature of traffic prevalent in such networks, and consequently, could lead to subpar resource utilization. In this paper, we fill this gap by investigating dynamic asymmetric subcarrier allocation and reconfiguration in drop-and-continue (D&C) optical networks. In particular, our approach aims at minimizing the operational cost of service provisioning by invoking reactive and coordinated connection consolidation as a remedy for the inability to accommodate traffic demands with in-service or newly activated P2MP-TRXs. We first devise an integer linear programming (ILP) model to solve the multi-objective optimization problem exactly. As the problem is proved to beNP-hard, we further develop a column generation (CG)-based approximation algorithm that can offer guaranteed optimality bounds within reasonable time, as well as a polynomial-time heuristic framework employing priority-queue-based progressive search. Extensive simulations verify the effectiveness of our proposal, demonstrating up to 43.3% reduction in bandwidth blocking ratio and 2.9% improvement in spectrum utilization compared with the state of the art.

Vision-language-action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training these models often relies on large-scale user-specific data, raising concerns about privacy and security, which in turn limits their broader adoption. To address this, we propose FedVLA, the first federated VLA learning framework, enabling distributed model training that preserves data privacy without compromising performance. Our framework integrates task-aware representation learning, adaptive expert selection, and expert-driven federated aggregation, enabling efficient and privacy-preserving training of VLA models. Specifically, we introduce an Instruction Oriented Scene-Parsing mechanism, which decomposes and enhances object-level features based on task instructions, improving contextual understanding. To effectively learn diverse task patterns, we design a Dual Gating Mixture-of-Experts (DGMoE) mechanism, where not only input tokens but also self-aware experts adaptively decide their activation. Finally, we propose an Expert-Driven Aggregation strategy at the federated server, where model aggregation is guided by activated experts, ensuring effective cross-client knowledge transfer.Extensive simulations and real-world robotic experiments demonstrate the effectiveness of our proposals. Notably, DGMoE significantly improves computational efficiency compared to its vanilla counterpart, while FedVLA achieves task success rates comparable to centralized training, effectively preserving data privacy.

Relation Extraction (RE) aims to identify relations between entities in text. Despite the potential of Large Language Models (LLMs) in RE, they struggle with low relevance of relations in retrieved demonstrations and inconsistent responses to identical queries. Therefore, we introduce OSLLM, a novel retrieve-reason-refine framework for RE in Open Source Intelligence with LLMs. OSLLM leverages relation embeddings for accurate retrieval, performs better in-context reasoning, and refines outputs via template self-optimization and answer self-evaluation. Extensive experiments demonstrate that our method outperforms other LLM-based methods in traditional, temporal, and open RE tasks. Additionally, leveraging OSLLM to assist fine-tuned models in handling boundary samples significantly boosts the performance of these smaller models.

Federated Learning (FL) enables multiple clients to collaboratively train a shared server model while preserving data privacy. Most existing FL systems rely on the assumption that the server model and client models have homogeneous architecture. However, intensive resource requirements during the training process prevent low-end devices from contributing to the server model with their own data. On the other hand, the resource constraints on participating clients can significantly limit the size of the server model in the model-homogeneous setting, thereby restricting the application scope of FL. In this work, we propose FedEKT, a novel model-heterogeneous FL system designed to obtain a high-performance large server model while benefiting heterogeneous small client models. Specifically, a new aggregation approach is designed to enable the integration of knowledge from heterogeneous client models to a large server model while mitigating the adverse effects of biases stemming from data heterogeneity. Subsequently, to enhance the performance of client models by benefiting from the high-performance server model, FedEKT distills this large server model into multiple heterogeneous client models, facilitating the transfer of integrated knowledge back to the client models. In addition, we design specialized modules within the model and communication strategy to accomplish aggregation and transfer of knowledge in a data-free manner. The evaluation results demonstrate that FedEKT enhances the accuracy of the server model and client models by up to 53.96% and 12.35%, respectively, compared with the state-of-the-art FL approach on CIFAR-100.

Personalized Federated Learning (PFL) aims to learn a custom model for each distributed client while benefiting from collaborative training in order to overcome the detrimental impact of data heterogeneity. Despite the promising benefits, the existing approaches often compromise the generalization performance of personalized models, as they solely focus on enhancing the personalization capability of models or merely aim to strike a balance between personalization and generalization. Indeed, increasing the personalization capability while preserving the strong generalization performance enabled by collaborative training remains a challenge for PFL, as the two objectives seem to compete with each other. To tackle this challenge, we investigate the relationship between model generalization and personalization under different degrees of heterogeneity. We find that besides the client-specific data distribution, the distribution bias between the unique data distribution of each client and that of the whole population is another critical factor that prominently impacts these two performances. Motivated by the above finding, we propose PFed-DBA, a novel PFL framework that effectively perceives this distribution bias to guide the training process. Concretely, we design the PFL models as a skip-connection network between a shared module for learning the shared representations delivering the common distribution of data across all clients and a personalized module for learning the personalized representations of the heterogeneous distribution bias. Then, we devise corresponding loss functions, aggregation strategy, and updating strategy in order to make the two modules intelligently complement each other. Moreover, we conduct extensive experiments to evaluate the effectiveness of PFed-DBA. The results show that PFed-DBA improves model accuracy to 12.34% at best compared with the state-of-the-art.

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same time.In this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

Abstract Within Open Relation Extraction (ORE) tasks, the Zero-shot ORE method is to generalize undefined relations from predefined relations, while the Unsupervised ORE method is to extract undefined relations without the need for annotations. However, despite the possibility of overlap between predefined and undefined relations in the training data, a unified framework for both Zero-shot and Unsupervised ORE has yet to be established. To address this gap, we propose U-CORE: A Unified Deep Cluster-wise Contrastive Framework for both Zero-shot and Unsupervised ORE, by leveraging techniques from Contrastive Learning (CL) and Clustering.1 U-CORE overcomes the limitations of CL-based Zero-shot ORE methods by employing Cluster-wise CL that preserves both local smoothness as well as global semantics. Additionally, we employ a deep-cluster-based updater that optimizes the cluster center, thus enhancing the accuracy and efficiency of the model. To increase the stability of the model, we adopt Adaptive Self-paced Learning that effectively addresses the data-shifting problems. Experimental results on three well-known datasets demonstrate that U-CORE significantly improves upon existing methods by showing an average improvement of 7.35% ARI on Zero-shot ORE tasks and 15.24% ARI on Unsupervised ORE tasks.

The cold-start problem, faced when providing recommendations to newly joined users with no historical interaction record existing in the platform, is one of the most critical problems that negatively impact the performance of a recommendation system. Fortunately, cross-domain recommendation~(CDR) is a promising approach for solving this problem, which can exploit the knowledge of these users from source domains to provide recommendations in the target domain. However, this method requires that the central server has the interaction behaviour data in both domains of all the users, which prevents users from participating due to privacy issues.

As a promising technology, Flex Ethernet (FlexE) helps to realize deterministic and ultra-low latency in metro and transport networks. Meanwhile, previous studies have confirmed the advantages of the symbiosis of FlexE and elastic optical network (EON) (i.e., a FlexE-over-EON) on resource utilization and cost-effectiveness. In this paper, we consider the cross-layer restoration (CLR) in FlexE-over-EONs based on the FlexE-aware architecture. Specifically, we address the situation where an outage happened on one FlexE switch in the packet layer to bring it offline temporarily and then the affected client flows need to be recovered quickly and proactively. Three CLR strategies are first proposed to fully explore the flexibility of FlexE-over-EON for restoring the affected flows. Then, with the strategies, we formulate an integer linear programming (ILP) model and design an auxiliary graph (AG) based algorithm to reroute the affected flows as well as minimize the additional operational expense (OPEX) incurred during the CLR. Extensive simulations verify the effectiveness of our proposed CLR algorithms.