Federated Learning (FL) is an emerging distributed learning framework for collaboratively training global models while protecting data privacy. However, FL faces serious threats from malicious attacks such as model poisoning and data poisoning. To address these challenges, this paper proposes FedMRA (MRA: Multimodal feature analysis and Reputation Aggregation). FedMRA establishes an anomaly detection index system through multimodal feature extraction, combines HDBSCAN clustering, historical trajectory sparsification analysis and dynamic reputation assessment mechanism, and adopts an aggregation strategy based on reputation weight fusion to defend against malicious client damage. Experiments show that FedMRA performs well against multiple types of attacks (label flipping attack, gaussian attack, and random gradient attack), especially when up to 30% malicious clients are involved, and still significantly improves the robustness and accuracy of the global model. FedMRA provides an efficient and robust defense scheme for FL, and it performs well in both no-attack and attack scenarios.

The core challenge in location privacy protection for location-based services (LBS) remains balancing location privacy and data utility. Differential privacy, backed by mathematical proofs, offers an effective framework for location protection. However, existing extended differential privacy methods for this purpose have some limitations. On one hand, most such methods focus on Euclidean spaces, making them ill-suited for road network-based LBS. They fail to align with road network contexts, potentially disrupting path planning, and often introduce excessive noise that inflates distance loss and degrades service quality. On the other hand, location semantics, a critical component of data utility, are frequently overlooked. Their degradation directly undermines utility. To address these issues, this paper introduces Semantic-Graph-Indistinguishability (SEM-G-IND) within the differential privacy paradigm, aiming to enhance location protection under road network and semantic constraints. First, a POI-based location semantic hash is designed to quantify location semantics. Then, integrating semantic distance and shortest path distance, a novel graph-based metric, Semantic-Graph-Distance (SGD), is proposed to measure inter-location distances. Finally, based on SGD, we propose a semantic penalty-based differential privacy (SPDP) location protection mechanism under road networks that satisfies ϵ-SEM-G-IND. We validated on real-world datasets that the SPDP mechanism effectively reduces the semantic loss of locations while ensuring minimal path distance loss, and it is feasible in terms of time overhead.

With the deep integration of computing and network development, as a new type of network infrastructure, computing power network (CPN) has become a research hotspot in the industry. Computing resource metrics integrates the computing resources connected to the CPN, realizes the collaborative management of heterogeneous resources through the measurement of multi-dimensional computing resource, and provides an accurate resource view for resource matching, which has become an important part of the CPN. The traditional measurement methods are too single to measure computing resources from a single dimension, which is difficult to adapt to the development of CPN. The existing methods of computing resource metrics need to be improved in the accuracy of resource matching and cannot reflect the comprehensive performance of computing resources. In this paper, a multi-dimensional computing resource measurement method based on entropy weight TOPSIS is designed to score the comprehensive performance of computing resources, storage resources and communication resources of computing nodes, then the nodes are divided into different categories of comprehensive performance according to the score, so as to narrow the scope of resource matching for different user requirements. At the same time, a multi-dimensional resource matching algorithm based on deep reinforcement learning is proposed. The resource matching process is constructed as a Markov decision process to realize the matching of tasks and nodes. The simulation results show that the proposed algorithm can better solve the matching problem of multi-dimensional resources, and the utilization rate of all kinds of resources reaches more than 90%.

Computing Power Network (CPN) overcome the performance limitations of computing power islands by integrating computing and network resources, dynamically scheduling business traffic to optimal nodes. However, effectively and collaboratively utilizing computing resources to reduce the delay of computing tasks has become a challenging issue in CPN due to the heterogeneity of resources and dynamic load. Existing works often treat workflow tasks as atomic tasks for offloading, disregarding subtask dependencies within a task. This approach leads to increased overall waiting time due to varying execution delays of each subtask. To address this problem, this paper proposes an optimization algorithm for workflow tasks based on slack quantity (CSA_WTO). The algorithm optimizes the arrival order of workflow task graphs before offloading, considering subtask dependencies and arranging them appropriately for subsequent offloading. This effectively reduces waiting delays for subtasks. Additionally, we utilize the Dependent Task Offloading algorithm (DTO) based on A3C to offload optimized workflow tasks, thereby improving execution efficiency in CPN. Simulation results demonstrate that compared with other algorithms, CSA_WTO significantly reduces task waiting delays by up to 85%, and DTO achieves a request acceptance rate of up to 96% while reducing the average completion time by 71%.

Traffic flow prediction assumes a pivotal role in aiding governments and companies accurately forecast changes in vehicle volume, consequently enhancing transportation efficiency and facilitating vehicle travel. Presently, the majority of traffic flow prediction methods rely on centralized learning strategies, which entail the transmission of substantial data and may jeopardize user privacy. To address this issue, we propose a Federated Learning-based Attention Graph Convolutional Network (FLSTAGCN) algorithm for traffic flow prediction. Firstly, we develop a Spatial-Temporal Attention Graph Convolutional Network (STAGCN) method that employs attention mechanism to proficiently extract spatial-temporal features from traffic flow data, augmenting the model's learning capabilities. Subsequently, within the aggregation mechanism of Federated learning, we devise a bespoke optimal selection to enhance training accuracy and reduce communication costs in traffic flow prediction scenarios. Finally, we integrate Federated Learning with STAGCN and utilize the optimal selection protocol to designate participants for transmitting optimal parameters. The Experimental results substantiate that our approach outperforms advanced deep learning approaches in terms of traffic flow prediction performance while ensuring the privacy and security of traffic data.

Federated learning is an emerging machine learning framework, which is commonly used in the structure of distributed machine learning due to its characteristic of “data immutable model motion”. In practical scenarios, the data samples and hardware conditions between clients are highly heterogeneous. The traditional simple aggregation can cause the global model to unintentionally favor certain clients. There is a significant performance gap between vulnerable groups and groups with richer training resources in the global model. This paper proposes Dynamic Momentum-based Federated Learning (DMFL) to address this issue. It dynamically adjusts the client aggregation weight based on historical performance and current round losses in each round. Experimental results show that DMFL can improve the effectiveness of the overall model while reducing the variance of the client accuracy distribution. Compared to existing baselines, the proposed algorithm performs superior fairness in results.

Nowadays, hybrid electric vehicles are becoming a major trend of future vehicle industry due to emission reduction and energy conversation requirements. As a key technology, the related energy management strategy is one of the research focus for the purpose of prolonging battery life and increasing the vehicle endurance. This paper proposed a fuzzy control based energy management strategy for precise control of the energy flow between the Lithium iron phosphate(LFP) battery and Ultra-Capacitor(UC) parallel hybrid electric vehicle. The proposed fuzzy controller takes the power requirement of electric vehicle and State Of Charge(SOC) of LFP battery and UC as three input parameters, a power allocation factor between LFP battery and UC is then determined as an output variable. The simulation result proves that the fuzzy control based energy management strategy helps eliminating the surge current and significantly improve the energy utilization rate.

The energy management is one of the most important issues for the efficiency and performance of the hybrid vehicular power system, including the Lithium-ion battery and Ultra-Capacitor. This paper deals with a dynamic programming based optimal control strategy proposed for the hybrid vehicular power system. The proposed method utilizes the capability of dynamic programming to treat the global optimization problem. Within the optimization process, the total energy consumption is the optimization target and the state of charge of ultra-capacitor is the state variable. When compared to the single energy system, the energy consumption of the hybrid energy system with dynamic programming can reduce 2.5% and the high instantaneous power is allocated to ultra-capacitor, which can reduce the damage to Lithium-ion battery. This paper proves that the dynamic programming algorithm can be applied to the hybrid energy system and presents a rather well performance.