ABSTRACT Federated learning (FL) has emerged as an impactful paradigm for privacy‐preserving machine learning, and allows model training without the need to share raw data. However, data heterogeneity across clients challenges practical FL deployment. Data space heterogeneity and statistical heterogeneity create significant training difficulties. System heterogeneity imposes additional external constraints. These combined factors impair convergence and reduce model performance. They also raise concerns regarding fairness, scalability and robustness. Focused on data heterogeneity, this review provides a structured analysis of FL. It encompasses three key areas: core categorizations of data heterogeneity, algorithmic advances (e.g., personalized FL, mixture‐of‐experts architectures, transfer learning‐based solutions) and system‐level techniques spanning communication optimization, resource adaptation and secure collaboration. We further synthesize benchmark efforts and real‐world applications in healthcare, finance, nuclear power and the Internet of Things (IoT)/edge computing to highlight the practical implications of heterogeneity‐aware FL. Finally, we identify key challenges and outline promising research directions towards scalable, fair and adaptive FL systems capable of operating in complex real‐world settings. This survey aims to serve as a reference point and conceptual roadmap for future research in heterogeneous FL.

Quantization and chroma downsampling are two primary operations that introduce distortions in the JPEG compression. However, most existing blind methods treat artifacts removal as a direct mapping from compressed images to clean ones. They fail to explicitly model the underlying degradation process or design targeted compensation mechanisms. As a result, these methods can only partially remove compression artifacts and struggle to generalize to diverse or unseen degradation scenarios. In this work, we present a novel perspective that formulates artifacts removal as an approximate inversion of the lossy steps in JPEG. Based on this view, we propose an Inverse JPEG Compression Network (IJCN), which aims to progressively compensate for quantization errors and color distortions. Specifically, we first design a Learnable Offset Guidance Module (LOGM) to approximate inverse quantization by modeling both intra-block and inter-block coefficient correlations for predicting rounding offsets. In addition, we propose a Quantization Table Guidance Module (QTGM) that leverages the quantization tables to guide the reconstruction network in mitigating color distortions. By modeling compensation mechanisms under the guidance of quantization tables, IJCN effectively eliminates artifacts across varying compression levels. Extensive experiments demonstrate that IJCN outperforms existing methods in both quantitative metrics and visual quality.

We investigate a multi-layer data transmission scheme with superimposed pilots (SIPs) to enhance the throughput of multiple-input multiple-output orthogonal frequency-division multiplexing systems. However, in multi-layer data transmission scenarios, signal coupling between different antennas and layers causes severe interference issues, posing significant challenges for receiver design. To address this issue, we propose a deep learning-based receiver architecture, named SANet, which leverages the parallel processing capabilities of the multi-head self-attention (MHSA) mechanism. Specifically, each head of the MHSA mechanism is used to extract local features from each layer of the received signal, enabling the separation and reception of multi-layer bitstream information. Additionally, a flexible and diverse data augmentation strategy is designed to enhance the generalization capability of the deep receiver. Numerical results show that, compared to traditional schemes, the proposed SANet with orthogonal pilots can improve throughput by 7.01%, while the proposed SANet with SIPs can improve throughput by 37.15%.

3D Gaussian Splatting (3DGS) has emerged as a high-fidelity and efficient paradigm for online free-viewpoint video (FVV) reconstruction, offering viewers rapid responsiveness and immersive experiences. However, existing online methods face challenge in prohibitive storage requirements primarily due to point-wise modeling that fails to exploit the motion properties. To address this limitation, we propose a novel Compact Gaussian Streaming (ComGS) framework, leveraging the locality and consistency of motion in dynamic scene, that models object-consistent Gaussian point motion through keypoint-driven motion representation. By transmitting only the keypoint attributes, this framework provides a more storage-efficient solution. Specifically, we first identify a sparse set of motion-sensitive keypoints localized within motion regions using a viewspace gradient difference strategy. Equipped with these keypoints, we propose an adaptive motion-driven mechanism that predicts a spatial influence field for propagating keypoint motion to neighboring Gaussian points with similar motion. Moreover, ComGS adopts an error-aware correction strategy for key frame reconstruction that selectively refines erroneous regions and mitigates error accumulation without unnecessary overhead. Overall, ComGS achieves a remarkable storage reduction of over 159 × compared to 3DGStream and 14 × compared to the SOTA method QUEEN, while maintaining competitive visual fidelity and rendering speed. Project page: https://chenjiacong-1005.github.io/ComGS/.

ABSTRACT No‐reference image quality assessment (NR‐IQA) has garnered significant attention due to its critical role in various image processing applications. This survey provides a comprehensive and systematic review of NR‐IQA methods, datasets, and challenges, offering new perspectives and insights for the field. Specifically, we propose a novel taxonomy for NR‐IQA methods based on distortion scenarios and design principles, which distinguishes this work from previous surveys. Representative methods within each category are thoroughly examined, with a focus on their strengths, limitations, and performance characteristics. Additionally, we review 20 widely used NR‐IQA datasets that serve as benchmarks for evaluating these methods, providing detailed information on the number of images, distortion types, and distortion levels for each dataset. Furthermore, we identify and discuss key challenges currently faced by NR‐IQA methods, such as handling diverse and complex distortions, ensuring generalisation across datasets and devices, and achieving real‐time performance. We also suggest potential future research directions to address these issues. In summary, this survey offers a comprehensive and systematic examination of NR‐IQA methods, datasets, and challenges, offering valuable insights and guidance for researchers and practitioners working in the NR‐IQA domain.

The learned image compression (LIC) technique has surpassed the state-of-the-art traditional codecs (H.266/VVC) in case of rate-distortion (R-D) performance. Its real-time deployments are far advanced. In order to achieve more flexible deployments, an LIC technique should be flexible in adjusting its computational complexity and rate as demanded by a situation and its environment. In this paper, we propose a unified Rate-Distortion-Complexity (R-D-C) framework for LIC under channel energy concentration criteria. Specifically, we first introduce an Energy Asymptotic Nonlinear Transformation (EANT) designed to directly concentrate on the channel energy of latent representations, thus laying the groundwork for a scalable entropy coding. Next, leveraging this energy concentration characteristic, we propose a corresponding Heterogeneous Scalable Entropy Model (HSEM) for flexibly scaling bitstreams as needed. Finally, utilizing the proposed EANT, we construct a fine-grained scalable codec for formulating, in combination with HSEM, a comprehensive scalable R-D-C framework under the energy concentration criteria. The obtained experimental results demonstrate that the proposed method could enable seamless transitions between 13 different widths of sub-models within a single network, allowing for fine-grained control over the model bitrate, complexity, and hardware inference time. Additionally, the proposed method exhibits competitive R-D performance compared to many existing methods.