This paper investigates resource allocation for providing heterogeneous users with customized virtual reality (VR) streaming services in a mobile edge computing (MEC) system. We introduce a quality of experience (QoE) metric that considers system latency, user attention levels, and preferred resolutions to measure user experience based on the Weber-Fechner Law. A QoE maximization problem is then formulated for resource allocation to optimize user experience. It is cast as a reinforcement learning problem, aiming to learn a generalized policy applicable across diverse user environments of different MEC servers. To solve the problem, we propose a FedPromptDT framework, which employs federated learning (FL) and prompt-based generative sequence modeling to pre-train a common decision model across MEC servers. FL addresses the issue of insufficient local MEC data while protecting user privacy during offline training. Meanwhile, by integrating user-environment cues and user-preferred allocation, the design of prompts enhances the model’s adaptability to various user environments during online execution. Extensive experimental evaluations demonstrate that FedPromptDT outperforms baseline methods, exhibiting remarkable adaptability and maintaining superior performance across various user environments.

Model averaging is a widely adopted technique in federated learning (FL) that aggregates multiple client models to obtain a global model. Remarkably, model averaging in FL yields a superior global model, even when client models are trained with non-convex objective functions and on heterogeneous local datasets. However, the rationale behind its success remains poorly understood. To shed light on this issue, we first visualize the loss landscape of FL over client and global models to illustrate their geometric properties. The visualization shows that the client models encompass the global model within a common basin, and interestingly, the global model may deviate from the basin's center while still outperforming the client models. To gain further insights into model averaging in FL, we decompose the expected loss of the global model into five factors related to the client models. Specifically, our analysis reveals that the global model loss after early training mainly arises fromi)the client model's loss on non-overlapping data between client datasets and the global dataset andii)the maximum distance between the global and client models. Based on the findings from our loss landscape visualization and loss decomposition, we propose utilizing iterative moving averaging (IMA) on the global model at the late training phase to reduce its deviation from the expected minimum, while constraining client exploration to limit the maximum distance between the global and client models. Our experiments demonstrate that incorporating IMA into existing FL methods significantly improves their accuracy and training speed on various heterogeneous data setups of benchmark datasets. Code is available athttps://github.com/TailinZhou/FedIMA.

Federated learning allows multiple clients to collaboratively train a model without exchanging their data, thus preserving data privacy. Unfortunately, it suffers significant performance degradation due to heterogeneous data at clients. Common solutions involve designing an auxiliary loss to regularize weight divergence or feature inconsistency during local training. However, we discover that these approaches fall short of the expected performance because they ignore the existence of avicious cyclebetween feature inconsistency and classifier divergence across clients. Thisvicious cyclecauses client models to be updated in inconsistent feature spaces with more diverged classifiers. To break thevicious cycle, we propose a novel framework namedFederated learning withFeatureAnchors(FedFA). FedFA utilizes feature anchors to align features and calibrate classifiers across clients simultaneously. This enables client models to be updated in a shared feature space with consistent classifiers during local training. Theoretically, we analyze the non-convex convergence rate of FedFA. We also demonstrate that the integration of feature alignment and classifier calibration in FedFA brings avirtuous cyclebetween feature and classifier updates, which breaks thevicious cycleexisting in current approaches. Extensive experiments show that FedFA significantly outperforms existing approaches on various classification datasets under label distribution skew and feature distribution skew.

Metaverse applications such as virtual reality (VR) content streaming, require optimal resource allocation strategies for mobile edge computing (MEC) to ensure a high-quality user experience. In contrast to online reinforcement learning (RL) algorithms, which can incur substantial communication overheads and longer delays, the majority of existing works employ offline-trained RL algorithms for resource allocation decisions in MEC systems. However, they neglect the impact of desynchronization between the physical and digital worlds on the effectiveness of the allocation strategy. In this paper, we tackle this desynchronization using a continual RL (CRL) framework that facilitates the resource allocation dynamically for MEC-enabled VR content streaming. We first design a digital twin-empowered edge computing (DTEC) system and formulate a quality of experience (QoE) maximization problem based on attention-based resolution perception. This problem optimizes the allocation of computing and bandwidth resources while adapting the attention-based resolution of the VR content. The CRL framework in DTEC enables adaptive online execution in a time-varying environment. We propose three variants of CRL, namely Continual Deep Deterministic Policy Gradient (CDDPG), Prioritized Experience Replay - CDDPG (PER-CDDPG), and Freshness Prioritized Experience Replay - CDDPG (FPER-CDDPG). We evaluate these algorithms, including two other benchmarks, using extensive experiments. FPER-CDDPG shows superior performance in terms of average latency, QoE, and successful delivery rate as well as meeting the hfQoE requirements over long-term execution while ensuring system scalability with the increasing number of users.

Mobile edge computing (MEC) resource allocation for remote rendering in virtual reality (VR) content streaming is critical for user experience. However, resource allocation becomes challenging due to the desynchronization between the physical and digital worlds in digital twin-empowered MEC. This paper presents our continual RL framework that facilitates dynamic resource allocation for MEC-enabled VR content streaming. We first design a digital twin-empowered edge computing (DTEC) system and formulate a maximization problem that considers attention-based resolution perception to maximize the quality of experience (QoE). This problem optimizes the allocation of computing and bandwidth resources while adapting the attention-based resolution of the VR content. We then apply continual reinforcement learning (CRL) to enable adaptive attention-based resolution VR streaming in a time-varying environment. We base the CRL's reward function on the QoE and horizon-fairness QoE (hfQoE) constraints. We support CRL with prioritized experience replay - continual deep deterministic policy gradient (PER-CDDPG) to enhance the performance of continual learning in the presence of time-varying DT updates. We test PER-CDDPG using extensive experiments and evaluation. PER-CDDPG outperforms the benchmarks in terms of average latency, QoE, and successful delivery rate as well as meeting the hfQoE requirements and performance over long-term execution while ensuring system scalability with the increasing number of users.

This article studies the online detection of false data injection attacks (FDIAs) and coordinated cyber-physical attacks (CCPAs) on power systems. By analyzing the hidden physical meaning of CCPAs, a cyber-physical FDIA with CCPA as a special case is proposed to establish the connection between FDIA and CCPA. Based on a discrete-time system dynamic model, an adaptive nonparametric cumulative sum (AN-CUSUM) detector is devised to deal with FDIAs and CCPAs simultaneously. The AN-CUSUM algorithm estimates the abnormal change of the state vector caused by attacks one step in advance and standardizes the decision statistics of conventional cumulative sum (CUSUM) to simplify the setting of thresholds. The proposed detector is robust to a wide range of time-varying system states and attack magnitudes. Moreover, a verification method is designed in the proposed detector to differentiate CCPAs and FDIAs according to unique characteristics of CCPAs, namely, the construction of CCPAs depends on the physical system parameter. Numerical results reveal that the proposed detector is more reliable to detect both FDIAs and CCPAs than existing CUSUM-based methods.