ABSTRACT In the context of security protection against false data injection attacks (FDIAs) in power grids, traditional federated learning effectively utilizes decentralized data resources for distributed training and achieves global collaboration. However, during the model aggregation process, it often overlooks or drowns out local sparse key features, significantly increasing the risk of missed detection of specific attack patterns. To address this issue, this paper proposes a personalized detection framework based on federated learning. Initially, the bidirectional transformer detection (BTD) model detection algorithm is deployed on the client side and trained on local data. Subsequently, through personalized federated learning, the client dynamically combines the weights of the global and local models to generate a personalized detection model. The framework employs a collaborative optimization mechanism of “global knowledge sharing and local feature adaptation” to effectively mitigate the feature drowning problem while strictly safeguarding data privacy. Compared to existing methods, this approach significantly enhances detection accuracy and robustness against differentiated attack patterns, thereby establishing a more reliable security defense system for smart grids.

This article revisits the federated policy gradient algorithm with environment heterogeneity for finding the optimal policy in multiagent reinforcement learning (RL). Toward this direction, personalized federated RL methods have been presented recently. However, existing personalized federated policy gradient methods may confine the personalized capacity of local policy models. In order to tackle this challenge, this article develops a provably convergent personalized federated first-order policy gradient algorithm, referred to as PfoPG, which learns a personalized policy model by adaptively mixing optimal global and local policies. Moreover, the momentum-based importance sampling is also introduced into PfoPG to improve its convergence speed. Meanwhile, this article rigorously analyzes the nonasymptotic convergence behavior of PfoPG. More specifically, PfoPG converges to a stationary policy with rateO(1/K), whereKdenotes the number of iterations. Compared to the state-of-the-art federated policy gradient methods, PfoPG can improve the convergence rate fromO(1/K2/3) toO(1/K). Finally, we verify the effectiveness of PfoPG by various experiments based on the multiagent particle environment.

Exogenous variables provide complementary information that enhances endogenous representations, thereby facilitating more accurate multivariate time series forecasting (MTSF). However, existing methods typically overlook the synergistic interplay between exogenous and endogenous variables by adopting shallow fusion strategies such as simple concatenation or separate encoding, which fail to capture the dynamic dependencies essential for modeling complex temporal patterns. To address this issue, we propose Endexformer, a novel hierarchical Endogenous-Exogenous modeling framework built upon the Transformer architecture. Specifically, Endexformer adopts a hierarchical architecture to jointly model temporal embeddings of endogenous variables and structural embeddings of exogenous variables, enabling a unified representation of cross-variable dependencies. To capture the fine-grained temporal patterns of endogenous variables, we present a multilevel temporal attention mechanism that leverages variable-level embeddings to adaptively incorporate exogenous information. Furthermore, we design a dynamic interactive attention mechanism that selectively emphasizes informative endogenous and exogenous patterns, mitigating redundancy and preserving semantic integrity in variable representations. Extensive experiments on eight real-world datasets show that Endexformer achieves outstanding performance against competing benchmark approaches in MTSF tasks across various temporal scenarios.

A pivotal problem in the Internet of Things (IoT) is resource allocation, where the goal is to optimize allocation strategies of IoT resources. In general, resource allocation problems are formulated as constrained optimization problems, which can be effectively solved by the zeroth-order Frank-Wolfe algorithm. However, the existing zeroth-order Frank-Wolfe algorithms suffer from slow convergence since they scale the zeroth-order gradient in all directions. For this reason, we propose a faster zeroth-order Frank-Wolfe algorithm, referred to as ZO-AdaSFW, which incorporates adaptive gradient methods and the variance-reduced technique (SPIDER) into the zeroth-order Frank-Wolfe algorithm. Moreover, ZO-AdaSFW can achieve the convergence rate of$O(T^{-1}) $in the convex setting, where T is the time horizon. Meanwhile, we also prove that ZO-AdaSFW has the best-known convergence rate$O(T^{-1/2})$among zeroth-order algorithms in the nonconvex setting. In addition, the experimental results show that the performance of ZO-AdaSFW outperforms state-of-the-art zeroth-order Frank-Wolfe algorithms on different applications.

Decentralized actor-critic (AC) is one of the most dominant algorithms for dealing with multiagent reinforcement learning (MARL) problems. However, exploration-efficient, sample-efficient, and communication-efficient are difficult to achieve simultaneously by existing decentralized AC methods. For this reason, this article develops a decentralized multiagent AC algorithm by incorporating entropy regularization to improve exploration with theoretical guarantees, referred to as multi-agent AC algorithm with entropy regularization (MACE). Moreover, we rigorously prove that MACE can achieve sample complexity $\mathcal {O}(\epsilon ^{-2}\ln \epsilon ^{-1})$ and communication complexity of $\mathcal {O}(\epsilon ^{-1}\ln \epsilon ^{-1})$ , which match the best complexities at present. Finally, the performance of MACE is also evaluated on reinforcement learning (RL) tasks. The experimental results show that the proposed algorithm achieves better exploration efficiency than state-of-the-art decentralized AC-type algorithms.

Federated meta-learning (ML) is a promising optimization framework for the intelligent Internet of Things (IoT). However, the generalization ability of existing federated ML is limited because it is a bilayer structure, which has a more complex loss landscape. Moreover, the loss landscape of bilevel optimization has more saddle points and sharp points, which may lead to different generalization performances. Therefore, how to choose an optimal point is crucial for improving the generalization ability of federated ML. For this reason, this article proposes a provable federated ML algorithm by using the sharpness-aware minimization technique, referred to as FedAvg-sharp-MAML (FSM). Furthermore, we rigorously analyse the convergence and generalization bound of FSM. Specifically, when local iteration rounds$T=1$, the rate of$O(1/K)$can be achieved, where K is the number of global iterations. Furthermore, this rate can match the Per-Fedavg method. Meanwhile, we achieve a better generalization bound than the state of the art federated ML, where PAC-Bayesian generalization bounds are introduced in our analysis. Finally, we conduct some experiments to verify the performance of FSM. The experimental results show that the FSM has good generalization performance compared to the existing federated ML algorithms.

Adaptive gradient algorithms have recently become extremely popular because they have been applied successfully in training deep neural networks, such as Adam, AMSGrad, and AdaBound. Despite their success, however, the distributed variant of the adaptive method, which is expected to possess a rapid training speed at the beginning and a good generalization capacity at the end, is rarely studied. To fill the gap, a distributed adaptive subgradient algorithm is presented, called D-AdaBound, where the learning rates are dynamically bounded by clipping the learning rates. Moreover, we obtain the regret bound of D-AdaBound, in which the objective functions are convex. Finally, we confirm the effectiveness of D-AdaBound by simulation experiments on different datasets. The results show the performance improvement of D-AdaBound relative to existing distributed online learning algorithms.

We consider decentralized large-scale continuous submodular constrained optimization problems over networks, where the goal is to maximize a sum of nonconvex functions with diminishing returns property. However, the computations of the projection step and the whole gradient can become prohibitive in high-dimensional constrained optimization problems. For this reason, a decentralized randomized block-coordinate Frank-Wolfe algorithm is proposed for submoduar maximization over networks by local communication and computation, which adopts the randomized block-coordinate descent and the Frank-Wolfe technique. We also show that the proposed algorithm converges to an approximation fact$(1-e^{-p_{\max }/p_{\min }})$of the global maximal points at a rate of$\mathcal {O}(1/T)$by choosing a suitable stepsize, where$T$is the number of iterations. In addition, we confirm the theoretical results by experiments.

This paper considers a decentralized online submodular maximization problem over time-varying networks, where each agent only utilizes its own information and the received information from its neighbors. To address the problem, we propose a decentralized Meta-Frank-Wolfe online learning method in the adversarial online setting by using local communication and local computation. Moreover, we show that an expected regret bound of $O(\sqrt{T})$ is achieved with $(1-1/e)$ approximation guarantee, where $T$ is a time horizon. In addition, we also propose a decentralized one-shot Frank-Wolfe online learning method in the stochastic online setting. Furthermore, we also show that an expected regret bound $O(T^{2/3})$ is obtained with $(1-1/e)$ approximation guarantee. Finally, we confirm the theoretical results via various experiments on different datasets.

Many problems in the Internet of Things (IoT) can be regarded as online optimization problems. For this reason, an online-constrained problem in IoT is considered in this article, where the cost functions change over time. To solve this problem, many projected online optimization algorithms have been widely used. However, the projections of these algorithms become prohibitive in problems involving high-dimensional parameters and massive data. To address this issue, we propose a Frank- Wolfe Adam online learning algorithm called Frank-Wolfe Adam (FWAdam), which uses a Frank-Wolfe method to eschew costly projection operations. Furthermore, we first give the convergence analysis of the FWAdam algorithm, and prove its regret bound to O(T3/4) when cost functions are convex, where T is a time horizon. Finally, we present simulated experiments on two data sets to validate our theoretical results.

The information name replaces traditional IP address as the identity of the network transmission is a typical feature of Information-Centric Networking (ICN). Therefore, designing efficient lookup algorithms of information names becomes a new challenge. For this reason, we propose an efficient name lookup structure for ICN, called Learned Bloom-Filter Lookup, which combines Recurrent Neural Networks (RNN) with standard Bloom filter to improve lookup efficiency. In our scheme, RNN trains the element set and non-element set, which are used to obtain the pre-filtering of names. Moreover, we look up the contents by using the backup Bloom filter, which can improve the accuracy of the search. In addition, we evaluate the performance of the proposed algorithm by experimental simulations. Compared with the Bloom-Hash method, our results show that our method can reduce the false positive rate. Furthermore, the memory required by our method is less than the Bloom-Hash method.

Mobile Microlearning, a novel fusion form of the mobile Internet, cloud computing, and microlearning, becomes more prevalent in recent years. However, its high deployment and operational costs make energy saving in cloud become a concerning issue. In this paper, to save energy consumption, a resource deployment approach to cloud service provision for Mobile Microlearning is proposed. Chinese Lexical Analysis System and Dynamic Term Frequency-Inverse Document Frequency (D-TF-IDF) are adopted to implement resource classification. Resources are deployed to the 2-tier cloud architecture according to the classification results. Grey Wolf Optimization (GWO) algorithm is used to forecast real-time energy consumption per byte. The simulation results show that, compared to traditional algorithm, the classification accuracy of small sample categories was significantly improved; the forecast energy consumption value and the standard values are 7.67% in private cloud and 2.93% in public cloud; the energy saving reaches 2.22% to 16.23% in 3G and 7.35% to 20.74% in Wi-Fi.

Computing offloading of mobile devices (MDs) through cloud is a greatly effective way to solve the problem of local resource constraints. However, cloud servers are usually located far away from MDs leading to a long response time. To this end, edge cloud servers (ECSs) provide a shorter response time due to being closer to MDs. In this paper, we propose a computing offloading game for MDs and ECSs. We prove the existence of a Stackelberg equilibrium in the game. In addition, we propose two algorithms, F‐SGA and C‐SGA, for delay‐sensitive and compute‐intensive applications, respectively. Moreover, the response time is reduced by F‐SGA, which makes decisions quickly. An optimal decision is obtained by C‐SGA, which achieves the equilibrium. Both algorithms above proposed can adjust the computing resource and utility of system users according to parameters control in computing offloading. The simulation results show that the game significantly saves the computing resources and response time of both the MD and the ECSs during the computing offloading process.

Routing in mobile ad hoc networks (MANETs) is an extremely challenging issue due to the features of MANETs. In this paper, we present a novel bio-inspired hybrid routing protocol (B-iHRP) supporting multimedia delivery based on zone routing framework, ant colony optimization (ACO) and physarum autonomic optimization (PAO). B-iHRP divides network topology into a series of zones subjectively. Within a zone, the route table of central node is proactively maintained by perceptive ants which can sense link status metrics through cross-layer perception to assess the discovered routes. Among zones, perceptive ants are sent to reactively find routes to destinations as well as assess the discovered routes with the metrics by source nodes. Afterwards, B-iHRP uses PAO to select the optimal one from the found routes and optimize autonomically the local routes during the course of multi-zone communication sessions. Simulation results show how B-iHRP can achieve the effective performance compared to existing state-of-the-art algorithms.

In this paper, we present a novel bio-inspired trusted routing framework (B-iTRF) which composed of trust mechanism and routing strategy. For trust mechanism, B-iTRF monitors neighbors' behavior in real time and then assesses neighbors' trust value based on the priori knowledge. For routing strategy, each node finds routes to the Sink based on ant colony optimization. In the process of path finding, B-iTRF senses and calculates the metrics of the found routes to support the route selection. Moreover, B-iTRF also assesses the availability of route based on Physarum autonomic optimization to maintain the route table. Simulation results show that B-iTRF can achieve the effective performance compared to existing state-of-the-art algorithms.

There is a trade-off between routing efficiency and energy equilibrium for sensor nodes in wireless sensor networks (WSNs). Inspired by the large and single-celled amoeboid organism-slime mold physarum polycephalum, this paper presents a novel physarum-inspired routing protocol (P-iRP) for WSNs to address the above issue. In P-iRP, a sensor node selects its proper next hop by using a proposed physarum-inspired selecting next hop model (PSN), which considers comprehensively the distance, energy residue and location of the next hop. We introduce the PSN's routing selecting strategy and detail PiRP's algorithms. Simulation results show how P-iRP can achieve the effective trade-off between routing efficiency and energy equilibrium compared to existing classical algorithms.