Towards addressing emerging energy challenges posed by unfair heterogeneous Semantic Communication (SC) codec updates within future wireless networks, this paper presents a novel Anti-discrimination Federated learning (AdFed) approach. Inspired by the economics of discrimination, unique fairness-associated energy concerns in SC systems are formulated as model discrimination challenges, with the SC-deployed wireless network conceptualized as an anti-discrimination labor market. A novel “affirmative action” strategy, based on training epochs, is proposed and adopted according to historical training unfairness results. To address the reverse discrimination issues in “affirmative action” caused by quota fairness impacting training energy cost, we formulate this problem as a coupled integer non-linear programming problem. Moreover, a new quota trade-off mechanism based on the Rubinstein bargaining game is also designed. Simulation results verify that AdFed outperforms SC training baselines, effectively addressing the unique model discrimination challenges of SC codec model heterogeneity updating. The efficacy of the game theoretical trade-off mechanism is demonstrated in achieving optimal outcomes.

To navigate the carbon emission and functional challenges associated with edge caching within heterogeneous Industrial Internet of Things (IIoT) spanning energy use, cache hit rate, and bandwidth usage, this paper proposes a novel Game Theory Empowered Carbon-Intelligent Federated Multi-Edge Caching framework (GT-FMC). The proposed framework enables distributed collaborative caching by intelligently coordinating edge nodes to optimize content decisions while efficiently integrating content providers (CPs), edge nodes, and users with energy-aware strategies. In GT-FMC, a lightweight federated content popularity prediction method based on Temporal Convolutional Networks (TCN) is introduced to collaboratively learn global content popularity while reducing prediction energy cost. The energy-aware utilities of the three involved parties are jointly formulated as a coupled non-linear optimization problem. To address this challenge, a two-stage game-theoretic algorithm is designed. Experimental results on a real-world testbed show that GT-FMC achieves up to 77.9% of Oracle in cache hit rate and 10.6%–32.4% reduction in transmission energy consumption compared to baseline methods. Complementary evaluations also validate the game-theoretic design’s effectiveness.

As a key technique for future networks, the performance of emerging multi-edge caching is often limited by inefficient collaboration among edge nodes and improper resource configuration. Meanwhile, achieving optimal cache hit rates poses substantive challenges without effectively capturing the potential relations between discrete user features and diverse content libraries. These challenges become further sophisticated when caching schemes are exposed to adversarial attacks that seriously impair cache performance. To address these challenges, we introduce RoCoCache, a resilient collaborative caching framework that uniquely integrates robust federated deep learning with proactive caching strategies, enhancing performance under adversarial conditions. First, we design a novel partitioning mechanism for multi-dimensional cache space, enabling precise content recommendations in user classification intervals. Next, we develop a new Discrete-Categorical Variational Auto-Encoder (DC-VAE) to accurately predict content popularity by overcoming posterior collapse. Finally, we create an original training mode and proactive cache replacement strategy based on robust federated deep learning. Notably, the residual-based detection for adversarial model updates and similarity-based federated aggregation are integrated to avoid the model destruction caused by adversarial updates, which enables the proactive cache replacement adapting to optimized cache resources and thus enhances cache performance. Using the real-world testbed and datasets, extensive experiments verify that the RoCoCache achieves higher cache hit rates and efficiency than state-of-the-art methods while ensuring better robustness. Moreover, we validate the effectiveness of the components designed in RoCoCache for improving cache performance via ablation studies.

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

Quantitative performance analysis plays a pivotal role in theoretically investigating the performance of Vehicular Edge Computing (VEC) systems. Although considerable research efforts have been devoted to VEC performance analysis, all of the existing analytical models were designed to derive the average system performance, paying insufficient attention to the worst-case performance analysis, which hinders the practical deployment of VEC systems to support mission-critical vehicular applications, such as collision avoidance. To bridge this gap, we develop an original performance analytical model by virtue of Stochastic Network Calculus (SNC) to investigate the worst-case end-to-end performance of VEC systems. Specifically, to capture the bursty feature of task generation, an innovative bivariate Markov Chain is firstly established and rigorously analysed to derive the stochastic task envelope. Then, an effective service curve is created to investigate the severe resource competition among vehicular applications. Driven by the stochastic task envelope and effective service curve, a closed-form end-to-end analytical model is derived to obtain the latency bound for VEC systems. Extensive simulation experiments are conducted to validate the accuracy of the proposed analytical model under different system configurations. Furthermore, we exploit the proposed analytical model as a cost-effective tool to investigate the resource allocation strategies in VEC systems.

Load prediction is an essential technique to improve edge system performance by proactively configuring and allocating system resources. Traditional load prediction methods obtain high prediction when handling loads exhibiting cyclical trend behavior, but they are unable to capturing highly-variable loads in edge computing environments. Existing studies fit prediction models via independent time series and output single-point real-value predictions. However, in practical edge scenarios, it is more valuable to obtain application value by utilizing the probability distribution of future loads rather than directly predicting specific values. To solve these problems, we propose an Edge Load Prediction method empowered by Deep Auto-regressive Recurrent networks (ELP-DAR). The ELP-DAR uses the time-series data of edge loads to train deep auto-regressive recurrent networks, which integrate Long Short-Term Memory (LSTM) into the S2S framework to calculate the parameters of the probability distribution at the next time-point. Therefore, the ELP-DAR can efficiently extract the essential representations of edge loads and learn their complex patterns, and the probability distribution for highly-variable edge loads can be accurately predicted. Extensive simulation experiments are conducted to validate the effectiveness of the proposed ELP-DAR method based on real-world edge load datasets. The results show that the ELP-DAR achieves higher prediction accuracy than other benchmark methods with different prediction lengths.

Establishing how a set of learners can provide privacy-preserving federated learning in a fully decentralized (peer-to-peer, no coordinator) manner is an open problem. We propose the first privacy-preserving consensus-based algorithm for the distributed learners to achieve decentralized global model aggregation in an environment of high mobility, where participating learners and the communication graph between them may vary during the learning process. In particular, whenever the communication graph changes, the Metropolis-Hastings method [ 69 ] is applied to update the weighted adjacency matrix based on the current communication topology. In addition, the Shamir’s secret sharing (SSS) scheme [ 61 ] is integrated to facilitate privacy in reaching consensus of the global model. The article establishes the correctness and privacy properties of the proposed algorithm. The computational efficiency is evaluated by a simulation built on a federated learning framework with a real-world dataset.

Temporal link prediction (TLP) is among the most important graph learning tasks, capable of predicting dynamic, time-varying links within networks. The key problem of TLP is how to explore potential link-evolving tendency from the increasing number of links over time. There exist three major challenges toward solving this problem: temporal nonlinear sparsity, weak serial correlation, and discontinuous structural dynamics. In this article, we propose a novel transfer learning model, called DNformer, to predict temporal link sequence in dynamic networks. The structural dynamic evolution is sequenced into consecutive links one by one over time to inhibit temporal nonlinear sparsity. The self-attention of the model is used to capture the serial correlation between the input and output link sequences. Moreover, our structural encoding is designed to obtain changing structures from the consecutive links and to learn the mapping between link sequences. This structural encoding consists of two parts: the node clustering encoding of each link and the link similarity encoding between links. These encodings enable the model to perceive the importance and correlation of links. Furthermore, we introduce a measurement of structural similarity in the loss function for the structural differences of link sequences. The experimental results demonstrate that our model outperforms other state-of-the-art TLP methods such as Transformer, TGAT, and EvolveGCN. It achieves the three highest AUC and four highest precision scores in five different representative dynamic networks problems.

With the advancement in Machine Learning (ML) techniques, a wide range of applications that leverage ML have emerged across research, industry, and society to improve application performance. However, existing ML schemes used within such applications struggle to attain high model accuracy due to the heterogeneous and distributed nature of their generated data, resulting in reduced model performance. In this paper we address this challenge by proposing PPFM: an adaptive and hierarchical Peer-to-Peer Federated Meta-learning framework. Instead of leveraging a conventional static ML scheme, PPFM uses multiple learning loops to dynamically self-adapt its own architecture to improve its training effectiveness for different generated data characteristics. Such an approach also allows for PPFM to remove reliance on a fixed centralized server in a distributed environment by utilizing peer-to-peer Federated Learning (FL) framework. Our results demonstrate PPFM provides significant improvement to model accuracy across multiple datasets when compared to contemporary ML approaches.

Over the past few years, fog radio access networks (F-RANs) have become a promising paradigm to support the tremendously increasing demands of multimedia services, by pushing computation and storage functionalities toward the edge of networks, closer to users. In F-RANs, distributed edge caching among fog access points (F-APs) can effectively reduce network traffic and service latency as it places popular contents at local caches of F-APs rather than the remote cloud. Due to the limited caching resources of F-APs and spatiotemporally fluctuant content demands from users, many cooperative caching schemes were designed to decide which contents are popular and how to cache them. However, these approaches often collect and analyze the data from Internet-of-Things (IoT) devices at a central server to predict the content popularity for caching, which raises serious privacy issues. To tackle this challenge, we propose a federated learning-based cooperative hierarchical caching scheme (FLCH), which keeps data locally and employs IoT devices to train a shared learning model for content popularity prediction. FLCH exploits horizontal cooperation between neighbor F-APs and vertical cooperation between the baseband unit (BBU) pool and F-APs to cache contents with different degrees of popularity. Moreover, FLCH integrates a differential privacy mechanism to achieve a strict privacy guarantee. Experimental results demonstrate that FLCH outperforms five important baseline schemes in terms of the cache hit ratio, while preserving data privacy. Moreover, the results show the effectiveness of the proposed cooperative hierarchical caching mechanism for FLCH.

Cellular communications have been regarded as promising approaches to deliver high-broadband communication links for unmanned aerial vehicles (UAVs), which have been widely deployed to conduct various missions, e.g., precision agriculture, forest monitoring, and border patrol. However, the unique features of aerial UAVs, including high-altitude manipulation, 3-D mobility, and rapid velocity changes, pose challenging issues to realize reliable cellular-enabled UAV communications, especially with the severe intercell interference generated by UAVs. To deal with this issue, we propose a novel position-based robust beamforming algorithm through complementarily integrating the navigation information and wireless channel information to improve the performance of cellular-enabled UAV communications. Specifically, in order to achieve the optimal beam weight vector, the navigation information of the UAV system is innovatively exploited to predict the changes of the direction-of-Arrival (DoA) angle. To fight against the high mobility of UAV operations, an optimization problem is formed by considering the tapered surface of DoA angle and solved to correct the inherent position error. Comprehensive simulation experiments are conducted and the results show that the proposed robust beamforming algorithm could achieve over 90% DoA estimation error reduction and up to 14-dB SINR gain compared with five benchmark beamforming algorithms, including linearly constrained minimum variance (LCMV), position-based beamforming, diagonal loading (DL), robust capon beamforming (RCB), and robust LCMV algorithm.

Content Caching at the edge of vehicular networks has been considered as a promising technology to satisfy the increasing demands of computation-intensive and latency-sensitive vehicular applications for intelligent transportation. The existing content caching schemes, when used in vehicular networks, face two distinct challenges: 1) Vehicles connected to an edge server keep moving, making the content popularity varying and hard to predict. 2) Cached content is easily out-of-date since each connected vehicle stays in the area of an edge server for a short duration. To address these challenges, we propose a Mobility-aware Proactive edge Caching scheme based on Federated learning (MPCF). This new scheme enables multiple vehicles to collaboratively learn a global model for predicting content popularity with the private training data distributed on local vehicles. MPCF also employs a Context-aware Adversarial AutoEncoder to predict the highly dynamic content popularity. Besides, MPCF integrates a mobility-aware cache replacement policy, which allows the network edges to add/evict contents in response to the mobility patterns and preferences of vehicles. MPCF can greatly improve cache performance, effectively protect users' privacy and significantly reduce communication costs. Experimental results demonstrate that MPCF outperforms other baseline caching schemes in terms of the cache hit ratio in vehicular edge networks.

To cope with the increasing content requests from emerging vehicular applications, caching contents at edge nodes is imperative to reduce service latency and network traffic on the Internet-of-Vehicles (IoV). However, the inherent characteristics of IoV, including the high mobility of vehicles and restricted storage capability of edge nodes, cause many difficulties in the design of caching schemes. Driven by the recent advancements in machine learning, learning-based proactive caching schemes are able to accurately predict content popularity and improve cache efficiency, but they need gather and analyse users' content retrieval history and personal data, leading to privacy concerns. To address the above challenge, we propose a new proactive caching scheme based on peer-to-peer federated deep learning, where the global prediction model is trained from data scattered at vehicles to mitigate the privacy risks. In our proposed scheme, a vehicle acts as a parameter server to aggregate the updated global model from peers, instead of an edge node. A dual-weighted aggregation scheme is designed to achieve high global model accuracy. Moreover, to enhance the caching performance, a Collaborative Filtering based Variational AutoEncoder model is developed to predict the content popularity. The experimental results demonstrate that our proposed caching scheme largely outperforms typical baselines, such as Greedy and Most Recently Used caching.

Multichannel communication is an important means to improve the reliability of low-power Internet-of-Things (IoT) networks. Typically, data transmissions in IoT networks are often required to be delivered before a given deadline, making deadline-driven channel allocation an essential task. The existing works on time-division multiple access often fail to establish channel schedules to meet the deadline requirement, as they often assume that transmissions can be successful within one transmission slot. Besides, the allocation and link estimation incur considerable overhead for the IoT nodes. In this article, we propose an edge-based channel allocation (ECA) for unreliable IoT networks. In ECA, we explicitly consider the impact of allocation sequences and employ a recurrent-neural-network-based channel estimation scheme. We utilize link quality and retransmission opportunities to maximize the packet delivery ratio before deadline. The allocation algorithms are executed on edge servers such that: 1) the channel allocation can be updated more frequently to deal with the wireless dynamics; 2) the allocation results can be obtained in real time; and 3) channel estimation can be more accurate. Extensive evaluation results show that ECA can significantly improve the reliability of deadline-driven IoT networks.

Content caching is a promising approach in edge computing to cope with the explosive growth of mobile data on 5G networks, where contents are typically placed on local caches for fast and repetitive data access. Due to the capacity limit of caches, it is essential to predict the popularity of files and cache those popular ones. However, the fluctuated popularity of files makes the prediction a highly challenging task. To tackle this challenge, many recent works propose learning based approaches which gather the users' data centrally for training, but they bring a significant issue: users may not trust the central server and thus hesitate to upload their private data. In order to address this issue, we propose a Federated learning based Proactive Content Caching (FPCC) scheme, which does not require to gather users' data centrally for training. The FPCC is based on a hierarchical architecture in which the server aggregates the users' updates using federated averaging, and each user performs training on its local data using hybrid filtering on stacked autoencoders. The experimental results demonstrate that, without gathering user's private data, our scheme still outperforms other learning-based caching algorithms such as m-epsilon-greedy and Thompson sampling in terms of cache efficiency.