The Internet of Things (IoT) ecosystem generates vast amounts of multimodal data from heterogeneous sources such as sensors, cameras, and microphones. As edge intelligence continues to evolve, IoT devices have progressed from simple data collection units to nodes capable of executing complex computational tasks. This evolution necessitates the adoption of distributed learning strategies to effectively handle multimodal data in an IoT environment. Furthermore, the real-time nature of data collection and limited local storage on edge devices in IoT call for an online learning paradigm. To address these challenges, we introduce the concept of Multimodal Online Federated Learning (MMO-FL), a novel framework designed for dynamic and decentralized multimodal learning in IoT environments. Building on this framework, we further account for the inherent instability of edge devices, which frequently results in missing modalities during the learning process. We conduct a comprehensive theoretical analysis under both complete and missing modality scenarios, providing insights into the performance degradation caused by missing modalities. To mitigate the impact of modality missing, we propose the Prototypical Modality Mitigation (PMM) algorithm, which leverages prototype learning to effectively compensate for missing modalities. Experimental results on two multimodal datasets further demonstrate the superior performance of PMM compared to benchmarks.

With the advancement of computational capabilities in edge devices such as intelligent sensors in the Industrial Internet of Things (IIoT), these sensors evolving beyond simple data collection to support complex computational tasks. This advancement provides new opportunities for adopting distributed learning approaches in IIoT. In this study, we focus on enhancing learning performance in an industrial assembly line scenario where multiple distributed sensors sequentially collect real-time data with distinct feature spaces. However, existing research lacks an online distributed learning framework tailored for such IIoT settings. To address this gap, we propose the Denoising and Adaptive Online Vertical Federated Learning (DAO-VFL) algorithm, a novel algorithm that leverages the computing potential of edge sensors while addressing key challenges such as communication overhead and data privacy. DAO-VFL effectively manages continuous data streams and adapts to shifting learning objectives. Furthermore, it can address critical challenges prevalent in industrial environment, such as communication noise and heterogeneity of sensor capabilities. To support the proposed algorithm, we provide a comprehensive theoretical analysis, highlighting the effects of noise reduction and adaptive local iteration decisions on the regret bound. Experimental results on two real-world datasets further demonstrate the superior performance of DAO-VFL compared to benchmarks.

In the evolving landscape of Web 3.0, deploying and scheduling decentralized applications (DApps) presents significant challenges due to the complexity of heterogeneous nodes, edges, and their intricate interactions. Traditional approaches, particularly graph-based reinforcement learning (RL) methods, often rely on homogeneous graphs, which fail to capture the diverse relationships inherent in heterogeneous Web 3.0 environments. This limitation results in inefficient resource allocation, suboptimal task scheduling, and unclear security requirements. To address these issues, this paper introduces the Heterogeneous Graph Deployment Scheduler (HGDS), a novel framework that leverages Heterogeneous Graph Neural Networks (HGNNs) to model users, edge servers, and DApp tasks within Web 3.0 environments, and incorporates RL to optimize DApp scheduling policies. HGDS captures heterogeneity in Web 3.0 environments by jointly modeling node–edge interactions and heterogeneous edge relationships, enabling the generation of dynamic, task-aware embeddings that integrate both node and edge features. RL is further employed to adaptively optimize scheduling and resource allocation based on real-time network feedback. Experiments on a Web 3.0 testbed show that HGDS outperforms baseline methods by 12.2% in reward, while reducing service delay and gas consumption.

With the increasing number and enhanced capabilities of IoT devices in smart buildings, these devices are evolving beyond basic data collection and control to actively participate in deep learning tasks. Federated Learning (FL), as a decentralized learning paradigm, is well-suited for such scenarios. However, the limited computational and communication resources of IoT devices present significant challenges. While existing research has extensively explored efficiency improvements in Horizontal FL, these techniques cannot be directly applied to Vertical FL due to fundamental differences in data partitioning and model structure. To address this gap, we propose a Lightweight Vertical Federated Learning (LVFL) framework that jointly optimizes computational and communication efficiency. Our approach introduces two distinct lightweighting strategies: one for reducing the complexity of the feature model to improve local computation, and another for compressing feature embeddings to reduce communication overhead. Furthermore, we derive a convergence bound for the proposed LVFL algorithm that explicitly incorporates both computation and communication lightweighting ratios. Experimental results on an image classification task demonstrate that LVFL effectively mitigates resource demands while maintaining competitive learning performance.

Federated learning (FL) is a new distributed machine learning frame-work known for its benefits on data privacy and communication efficiency. Since full client participation in many cases is infeasible due to constrained resources, partial participation FL algorithms have been investigated that proactively select/sample a subset of clients, aiming to achieve learning performance close to the full participation case. This paper studies a passive partial client participation scenario that is much less well understood, where partial participation is a result of external events, namely client dropout, rather than a decision of the FL algorithm. We cast FL with client dropout as a special case of a larger class of FL problems where clients can submit substitute (possibly inaccurate) local model updates. Based on our convergence analysis, we develop a new algorithm FL-FDMS that discovers friends of clients (i.e., clients whose data distributions are similar) on-the-fly and uses friends’ local updates as substitutes for the dropout clients, thereby reducing the substitution error. Experiments on MNIST and CIFAR-10 confirmed the superior performance of FL-FDMS in handling client dropout in FL.

Versatile video coding (VVC) aims to achieve high compression but also issues like varying content/network conditions. Existing rate control (RC) methods struggle to achieve optimal quality under these complex scenarios. This paper proposes a novel RC scheme for VVC based on a linear model. The Lagrange minimization multiplier is introduced under bit budget constraints, allowing optimized bit allocation. RC optimization is formulated as a convex solution, and is derived into the optimal quantization parameter (QP) for RC. Experimental analysis demonstrates the proposed linear model-based RC algorithm performances are better compared to other state-of-the-art methods due to their use of a linear model and optimal QP determination.

In this study, we address the emerging field of streaming federated learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning (FL) relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, leading to discrepancies between the local training data set and long-term distribution. To mitigate this problem, we propose three local cache update rules—first-in–first-out (FIFO), static ratio selective replacement (SRSR), and dynamic ratio selective replacement (DRSR)—that update the local cache of each client while considering the limited cache capacity. Furthermore, we derive a convergence bound for our proposed SFL algorithm as a function of the distribution discrepancy between the long-term data distribution and the client’s local training data set. We then evaluate our proposed algorithm on two data sets: 1) a network traffic classification data set and 2) an image classification data set. Our experimental results demonstrate that our proposed local cache update rules significantly reduce the distribution discrepancy and outperform the baseline methods. Our study advances the field of SFL and provides practical cache management solutions in FL.

Video coding is a video compression technique that compresses the original video sequence to produce a smaller archive file or reduce the transmission bandwidth under constraints on the visual quality loss. Rate control (RC) plays a critical role in video coding. It can achieve stable stream output in practical applications, especially real-time video applications such as video conferencing or game live streaming. Most RC algorithms either directly or indirectly characterise the relationship between the bit rate (R) and quantisation (Q) and then allocate bits to every coding unit so as to guarantee the global bit rate and video quality level. This paper comprehensively reviews the classic RC technologies used in international video standards of past generations, analyses the mathematical models and implementation mechanisms of various schemes, and compares the performance of recent state-of-the-art RC algorithms. Finally, we discuss future directions and new application areas for RC methods. We hope that this review can help support the development, implementation, and application of RC for new video coding standards.

This paper studies a federated learning (FL) system, wheremultipleFL services co-exist in a wireless network and share common wireless resources. It fills the void of wireless resource allocation for multiple simultaneous FL services in the existing literature. Our method designs a two-level resource allocation framework comprisingintra-serviceresource allocation andinter-serviceresource allocation. The intra-service resource allocation problem aims to minimize the length of FL rounds by optimizing the bandwidth allocation among the clients of each FL service. Based on this, an inter-service resource allocation problem is further considered, which distributes bandwidth resources among multiple simultaneous FL services. We consider both cooperative and selfish providers of the FL services. For cooperative FL service providers, we design a distributed bandwidth allocation algorithm to optimize the overall performance of multiple FL services, meanwhile catering it to the fairness among FL services and the privacy of clients. For selfish FL service providers, a new auction scheme is designed with the FL service providers as the bidders and the network operator as the auctioneer. The designed auction scheme strikes a balance between the overall FL performance and fairness. Our simulation results show that the proposed algorithms outperform other benchmarks under various network conditions.

This paper studies federated learning (FL) in a classic wireless network, where learning clients share a common wireless link to a coordinating server to perform federated model training using their local data. In such wireless federated learning networks (WFLNs), optimizing the learning performance depends crucially on how clients are selected and how bandwidth is allocated among the selected clients in every learning round, as both radio and client energy resources are limited. While existing works have made some attempts to allocate the limited wireless resources to optimize FL, they focus on the problem in individual learning rounds, overlooking an inherent yet critical feature of federated learning. This paper brings a new long-term perspective to resource allocation in WFLNs, realizing that learning rounds are not only temporally interdependent but also have varying significance towards the final learning outcome. To this end, we first design data-driven experiments to show that different temporal client selection patterns lead to considerably different learning performance. With the obtained insights, we formulate a stochastic optimization problem for joint client selection and bandwidth allocation under long-term client energy constraints, and develop a new algorithm that utilizes only currently available wireless channel information but can achieve long-term performance guarantee. Experiments show that our algorithm results in the desired temporal client selection pattern, is adaptive to changing network environments and far outperforms benchmarks that ignore the long-term effect of FL.