Federated graph learning (FGL) combines the advantages of graph neural networks and federated learning, extracting valuable information from decentralized graph data while preserving data privacy. Existing FGL methods typically consider mitigating the effect of non-IID under the class balanced assumption. However, in many real-world scenarios, the data class distribution often exhibits a long-tailed distribution, which can lead to the model being severely biased towards the head classes, thus affecting the overall performance of the global model. To address the challenges posed by non-IID and long-tailed distribution data in FGL, this paper proposes a method called FedGAC to train a globally balanced model. In the local stage, FedGAC employs feature-structure decoupled graph attention network to train client models, introduces class-importance balanced strategy to mitigate class imbalance issues, and leverages knowledge relearning strategy to enhance model generalization. In the global stage, FedGAC employs an adaptive gradients aggregation strategy that dynamically adjusts balancing factors based on the degree of class imbalance in each client’s data, thereby optimizing the aggregation process. Additionally, it utilizes global class gradients to calibrate learnable features. Experimental results demonstrate that, compared to the state-of-the-art federated learning methods, FedGAC achieves superior performance on highly non-IID and long-tailed distribution datasets.

With the emergence of parallel computing systems and distributed time-sensitive applications, it is urgent to provide statistical guarantees for age of information (AoI) in wireless cyber-physical systems (WCPS) with diverse parallelisms. However, most of the existing research on AoI have tended to focus on serial transmission, and the AoI performance of multi-stage parallel systems remains unclear. To help address these research gaps, in this work, we set out to investigate the age of event (AoE) violation probability in a three-stage WCPS with diverse parallelisms such as fork-join and split-merge. We analyze both transient and steady-state characteristics of AoE violation probability (AoEVP). Using these characteristics, we transform the AoEVP minimization problem into an equivalent minimization problem. Moreover, we develop a queuing model to capture the queue dynamics under the max-plus theory of stochastic network calculus (SNC) approach. Based on the max-plus model, we derive a closed-form Chernoff upper bound for the equivalent problem by applying the union bound and the Chernoff inequality. Furthermore, we characterize the service process for different parallelisms applicable to each stage. By solving the Chernoff upper bound with the service moment generation functions (MGFs), we obtain heuristic update period solutions for minimizing the AoEVP of three-stage WCPS. Simulation results validate our analysis and demonstrate that our heuristic update period solutions are near optimal for minimizing the AoEVP of three-stage WCPS with diverse parallelisms. Through the theoretical framework, we further provide insights into how such networks can be designed for better age performance.

Given the network and the time-triggered flow requests of a Time Sensitive Network (TSN), configuring the gate control lists (GCL) of IEEE 802.1Qbv for the ports of each node can be formed as a Job Shop Scheduling Problem, which is NP-hard. At present, most of the existing heuristic solutions for such problems consider scenarios where all given traffic flows can be scheduled. In order to solve the undetermined flow scheduling problem in scenarios no matter whether the flows can be scheduled or not, we propose to maximize the remaining time in conjunction with optimizing the network utilization instead of only minimizing the flowspan. Though the new problem is still NP-hard, it is a unified framework capable of covering general scenarios. On the basis of the new framework, we propose a novel Mixed initial population Genetic Algorithm (MGA) to solve the problem. Extensive simulation evaluation shows that MGA performs better and faster in different network scenarios while other methods prevails only in specific scenarios. This feature makes the method attractive in realistic TSN scheduling applications for in most cases it is hard for users to properly classifying the problem.