ICCV 2025 Papers

Federated Learning (FL) studies often assume a static data distribution, whereas real-world scenarios involve dynamic changes. To address this gap, we study Federated Continuous Category Discovery and Learning (FC^2DL), an essential yet underexplored problem that enables FL models to evolve continuously by discovering and learning novel data categories. The key challenge in FC^2DL lies in merging and aligning categories discovered and learned by different clients, all while maintaining privacy. To tackle this, we propose the Global Prototype Alignment (GPA) framework. GPA first estimates the number of categories and constructs global prototypes by locating high-density regions in the representation space through bi-level clustering. To mitigate pseudo-label noise, GPA then employs a semantic-weighted loss to capture correlations between global prototypes and the novel data. This semantic weighting strategy is also used for contrastive loss, facilitating unsupervised novel-category learning. Besides, GPA incorporates a mixup-based mechanism for both data and models, effectively mitigating interference between known and novel categories while alleviating forgetting. Extensive experiments across multiple datasets demonstrate GPA's superiority over state-of-the-art baseline approaches. Notably, GPA achieves absolute gains of 5.7% to 13.1% in novel category accuracy while preserving known category performance. Furthermore, GPA is highly adaptable, equipping various mainstream FL algorithms with category discovery and learning capabilities, underscoring its potential for real-world deployment.

A vast amount of instruction tuning data is crucial for the impressive performance of Large Multimodal Models (LMMs), but the associated computational costs and data collection demands during supervised fine-tuning make it impractical for most researchers. Federated learning (FL) has the potential to leverage all distributed data and training resources to reduce the overhead of joint training. However, most existing methods assume a fixed number of tasks, while in real-world scenarios, clients continuously encounter new knowledge and often struggle to retain old tasks due to memory constraints. In this work, we introduce the Federated Continual Instruction Tuning (FCIT) benchmark to model this real-world challenge. Our benchmark includes two realistic scenarios, encompassing four different settings and twelve carefully curated instruction tuning datasets. To address the challenges posed by FCIT, we propose dynamic knowledge organization to effectively integrate updates from different tasks during training and subspace selective activation to allocate task-specific output during inference. Extensive experimental results demonstrate that our proposed method significantly enhances model performance across varying levels of data heterogeneity and catastrophic forgetting. Code and dataset are released at https://github.com/Ghy0501/FCIT.

Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables collaborative model training without raw data sharing, it suffers from performance degradation when client data distributions exhibit statistical heterogeneity. We introduce FedXDS (Federated Learning via XAI-guided Data Sharing), the first approach to utilize feature attribution techniques to identify precisely which data elements should be selectively shared between clients to mitigate heterogeneity. By employing propagation-based attribution, our method identifies task-relevant features through a single backward pass, enabling selective data sharing that aligns client contributions. To protect sensitive information, we incorporate metric privacy techniques that provide formal privacy guarantees while preserving utility. Experimental results demonstrate that our approach consistently achieves higher accuracy and faster convergence compared to existing methods across varying client numbers and heterogeneity settings. We provide theoretical privacy guarantees and empirically demonstrate robustness against both membership inference and feature inversion attacks. Code is available at https://github.com/MaxH1996/FedXDS.

Federated learning (FL) often suffers from performance degradation due to key challenges such as data heterogeneity and communication constraints. To address these limitations, we present a novel FL framework called FedWSQ, which integrates weight standardization (WS) and the proposed distribution-aware non-uniform quantization (DANUQ). WS enhances FL performance by filtering out biased components in local updates during training, thereby improving the robustness of the model against data heterogeneity and unstable client participation. In addition, DANUQ minimizes quantization errors by leveraging the statistical properties of local model updates. As a result, FedWSQ significantly reduces communication overhead while maintaining superior model accuracy. Extensive experiments on FL benchmark datasets demonstrate that FedWSQ consistently outperforms existing FL methods across various challenging FL settings, including extreme data heterogeneity and ultra-low-bit communication scenarios. The source code is available at our project page.

Vision-Language-Action (VLA) models have significantly advanced robotic manipulation by enabling robots to interpret language instructions for task execution. However, training these models often relies on large-scale user-specific data, raising concerns about privacy and security, which in turn limits their broader adoption. To address this, we propose FedVLA, the first federated VLA learning framework, enabling distributed model training that preserves data privacy without compromising performance. Our framework integrates task-aware representation learning, adaptive expert selection, and expert-driven federated aggregation, enabling efficient and privacy-preserving training of VLA models. Specifically, we introduce an Instruction-Oriented Scene-Parsing mechanism, which decomposes and enhances object-level features based on task instructions, improving contextual understanding. To effectively learn diverse task patterns, we design a Dual Gating Mixture-of-Experts (DGMoE) mechanism, where not only input tokens but also self-aware experts adaptively decide their activation. Finally, we propose an Expert-Driven Aggregation strategy at the federated server, where model aggregation is guided by activated experts, ensuring effective cross-client knowledge transfer. Extensive simulations and real-world robotic experiments demonstrate the effectiveness of our proposals. Notably, DGMoE significantly improves computational efficiency compared to its vanilla counterpart, while FedVLA achieves task success rates comparable to centralized training, effectively preserving data privacy.

Federated learning (FL) enables collaborative training of a global model in the centralized server with data from multiple parties while preserving privacy. However, data heterogeneity can significantly degrade the performance of the global model when each party uses datasets from different sources to train a local model, thereby affecting personalized local models. Among various cases of data heterogeneity, feature drift, feature space difference among parties, is prevalent in real-life data but remains largely unexplored. Feature drift can distract feature extraction learning in clients and thus lead to poor feature extraction and classification performance. To tackle the problem of feature drift in FL, we propose FedPall, an FL framework that utilizes prototype-based adversarial learning to unify feature spaces and collaborative learning to reinforce class information within the features. Moreover, FedPall leverages mixed features generated from global prototypes and local features to enhance the global classifier with classification-relevant information from a global perspective. Evaluation results on three representative feature-drifted datasets demonstrate FedPall's consistently superior performance in classification with feature-drifted data in the FL scenario.

Neural fields provide a memory-efficient representation of data, which can effectively handle diverse modalities and large-scale data. However, learning to map neural fields often requires large amounts of training data and computations, which can be limited to resource-constrained edge devices. One approach to tackle this limitation is to leverage Federated Meta-Learning (FML), but traditional FML approaches suffer from privacy leakage. To address these issues, we introduce a novel FML approach called FedMeNF. FedMeNF utilizes a new privacy-preserving loss function that regulates privacy leakage in the local meta-optimization. This enables the local meta-learner to optimize quickly and efficiently without retaining the client's private data. Our experiments demonstrate that FedMeNF achieves fast optimization speed and robust reconstruction performance, even with few-shot or non-IID data across diverse data modalities, while preserving client data privacy.

In federated learning, textual prompt tuning adapts Vision-Language Models (e.g., CLIP) by tuning lightweight input tokens (or prompts) on local client data, while keeping network weights frozen. After training, only the prompts are shared by the clients with the central server for aggregation. However, textual prompt tuning suffers from overfitting to known concepts, limiting its generalizability to unseen concepts. To address this limitation, we propose Multimodal Visual Prompt Tuning (FedMVP) that conditions the prompts on multimodal contextual information - derived from the input image and textual attribute features of a class. At the core of FedMVP is a PromptFormer module that synergistically aligns textual and visual features through a cross-attention mechanism. The dynamically generated multimodal visual prompts are then input to the frozen vision encoder of CLIP, and trained with a combination of CLIP similarity loss and a consistency loss. Extensive evaluation on 20 datasets, spanning three generalization settings, demonstrates that FedMVP not only preserves performance on in-distribution classes and domains, but also displays higher generalizability to unseen classes and domains, surpassing state-of-the-art methods by a notable margin of +1.57% - 2.26%. Code is available at https://github.com/mainaksingha01/FedMVP.

Federated learning aims at training models collaboratively across participants while protecting privacy. However, one major challenge for this paradigm is the data heterogeneity issue, where biased data preferences across multiple clients, harming the model's convergence and performance. In this paper, we first introduce powerful diffusion models into the federated learning paradigm and show that diffusion representations are effective steers during federated training. To explore the possibility of using diffusion representations in handling data heterogeneity, we propose a novel diffusion-inspired Federated paradigm with Diffusion Representation Collaboration, termed FedDifRC, leveraging meaningful guidance of diffusion models to mitigate data heterogeneity. The key idea is to construct text-driven diffusion contrasting and noise-driven diffusion regularization, aiming to provide abundant class-related semantic information and consistent convergence signals. On the one hand, we exploit the conditional feedback from the diffusion model for different text prompts to build a text-driven contrastive learning strategy. On the other hand, we introduce a noise-driven consistency regularization to align local instances with diffusion denoising representations, constraining the optimization region in the feature space. In addition, FedDifRC can be extended to a self-supervised scheme without relying on any labeled data. We also provide a theoretical analysis for FedDifRC to ensure convergence under non-convex objectives. The experiments on different scenarios validate the effectiveness of FedDifRC and the efficiency of crucial components.

Federated Continual Learning (FCL) has emerged as a prominent distributed learning paradigm and aims at addressing model learning challenges in both federated and continual learning settings. Efficient personalization in FCL remains a major challenge, as it must handle not only conflicts between old and new knowledge within parallel task streams but also heterogeneous knowledge conflicts from different clients. Recent approaches attempt to mitigate these issues through gradient correction. However, they often overlook the combined impact of gradient magnitude and direction, leading to unsatisfactory gradient solutions. To address these issues, we propose a novel federated continual learning method (called FedAGC) with asymmetric gradient correction, which performs memory rehearsal using representative samples selected via a centroid-based approach from historical tasks. By formulating the problem as a multi-objective optimization problem, FedAGC derives more effective gradients while incorporating group-level personalization to facilitate useful knowledge integration and irrelevant knowledge isolation, effectively mitigating both temporal and spatial catastrophic forgetting. Extensive experiments confirm the effectiveness of FedAGC.

Training-free open-vocabulary semantic segmentation has advanced with vision-language models like CLIP, which exhibit strong zero-shot abilities. However, CLIP's attention mechanism often wrongly emphasises specific image tokens, namely outliers, which results in irrelevant over-activation. Existing approaches struggle with these outliers that arise in intermediate layers and propagate through the model, ultimately degrading spatial perception. In this paper, we propose a Self-adaptive Feature Purifier framework (SFP) to suppress propagated outliers and enhance semantic representations for open-vocabulary semantic segmentation. Specifically, based on an in-depth analysis of attention responses between image and class tokens, we design a self-adaptive outlier mitigator to detect and mitigate outliers at each layer for propagated feature purification. In addition, we introduce a semantic-aware attention enhancer to augment attention intensity in semantically relevant regions, which strengthens the purified feature to focus on objects. Further, we introduce a hierarchical attention integrator to aggregate multi-layer attention maps to refine spatially coherent feature representations for final segmentation. Our proposed SFP enables robust outlier suppression and object-centric feature representation, leading to a more precise segmentation. Extensive experiments show that our method achieves state-of-the-art performance and surpasses existing methods by an average of 4.6% mIoU on eight segmentation benchmarks. The code will be released.

The pretrain-finetune paradigm of pre-training a model on large amounts of image and text data and then fine-tuning the model for a specific task has led to significant progress in many 2D image and natural language processing tasks.Similarly, the use of pre-training methods in point cloud data can also enhance the working performance and generalization ability of the model.Therefore, in this paper, we propose a pre-training framework based on a diffusion model called PreDifPoint. It is able to accomplish the pre-training of the model's backbone network through a diffusion process of gradual denoising. We aggregate the potential features extracted from the backbone network, input them as conditions into the subsequent diffusion model, and direct the point-to-point mapping relationship of the noisy point clouds at neighboring time steps, so as to generate high-quality point clouds and at the same time better perform various downstream tasks of the point clouds.We also introduce a bi-directional covariate attention (DXCA-Attention) mechanism for capturing complex feature interactions, fusing local and global features, and improving the detail recovery of point clouds.In addition, we propose a density-adaptive sampling strategy, which can help the model dynamically adjust the sampling strategy between different time steps, and guide the model to pay more attention to the denser regions in the point cloud, thus improving the effectiveness of the model in point cloud recovery.Our PreDifPoint framework achieves more competitive results on various real-world datasets. Specifically, PreDifPoint achieves an overall accuracy of 87.96%, which is 0.35% higher than PointDif, on the classification task on PB-T50-395RS, a variant of ScanObjectNN dataset.

Large models have achieved remarkable performance across various tasks, yet they incur significant computational costs and privacy concerns during both training and inference. Distributed deployment has emerged as a potential solution, but it necessitates the exchange of intermediate information between model segments, with feature representations serving as crucial information carriers. To optimize information exchange, feature coding is required to reduce transmission and storage overhead. Despite its importance, feature coding for large models remains an under-explored area. In this paper, we draw attention to large model feature coding and make three fundamental contributions. First, we introduce a comprehensive dataset encompassing diverse features generated by three representative types of large models. Second, we establish unified test conditions, enabling standardized evaluation pipelines and fair comparisons across future feature coding studies. Third, we introduce two baseline methods derived from widely used image coding techniques and benchmark their performance on the proposed dataset. These contributions aim to provide a foundation for future research and inspire broader engagement in this field. To support a long-term study, all source code and the dataset are made available at https://github.com/chansongoal/LaMoFC.

Recent works on accelerating Vision-Language Models achieve strong performance across a variety of vision-language tasks despite highly compressing visual information. In this work, we examine the popular acceleration approach of early pruning of visual tokens inside the language model. Surprisingly, we find that while strong performance is maintained across many tasks, it exhibits drastically different behavior for a subset of vision-centric tasks such as localization. Upon further investigation, we uncover a core issue with the acceleration approach where most tokens towards the top of the image are pruned away. Yet, on many benchmarks aiming to evaluate vision-centric capabilities, strong performance persists with the flawed pruning strategy, highlighting these benchmarks' limited ability to assess fine-grained visual capabilities. Based on these findings, we propose FEATHER (Fast and Effective Acceleration wiTH Ensemble cRiteria), a straightforward approach that resolves the discovered early-layer pruning issue and further enhances the preservation of relevant tokens via multistage pruning with early uniform sampling to ensure broad image coverage. With comparable computational savings, we find that FEATHER achieves more than 5x performance improvement on the vision-centric localization benchmarks compared to the original acceleration approach.

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, demonstrating remarkable capability in high-fidelity scene reconstruction through its Gaussian primitive representations. However, the computational overhead induced by the massive number of primitives poses a significant bottleneck to training efficiency. To overcome this challenge, we propose Group Training, a simple yet effective strategy that organizes Gaussian primitives into manageable groups, optimizing training efficiency and improving rendering quality. This approach shows universal compatibility with existing 3DGS frameworks, including vanilla 3DGS and Mip-Splatting, consistently achieving accelerated training while maintaining superior synthesis quality. Extensive experiments reveal that our straightforward Group Training strategy achieves up to 30% faster convergence and improved rendering quality across diverse scenarios. Project Website: https://chengbo-wang.github.io/3DGS-with-Group-Training/.

Visual Autoregressive (VAR) modeling has gained popularity for its shift towards next-scale prediction. However, existing VAR paradigms process the entire token map at each scale step, leading to the complexity and runtime scaling dramatically with image resolution. To address this challenge, we propose FastVAR, a post-training acceleration method for efficient resolution scaling with VARs. Our key finding is that the majority of latency arises from the large-scale step where most tokens have already converged. Leveraging this observation, we develop the cached token pruning strategy that only forwards pivotal tokens for scale-specific modeling while using cached tokens from previous scale steps to restore the pruned slots. This significantly reduces the number of forwarded tokens and improves the efficiency at larger resolutions. Experiments show the proposed FastVAR can further speedup FlashAttention-accelerated VAR by 2.7x with negligible performance drop of <1%. We further extend \NAME to zero-shot generation of higher resolution images. In particular, FastVAR can generate one 2K image with 15GB memory footprints in 1.5s on a single NVIDIA 3090 GPU.

Deep neural networks have revolutionized 3D point cloud processing, yet efficiently handling large and irregular point clouds remains challenging. To tackle this problem, we introduce FastPoint, a novel software-based acceleration technique that leverages the predictable distance trend between sampled points during farthest point sampling. By predicting the distance curve, we can efficiently identify subsequent sample points without exhaustively computing all pairwise distances. Our proposal substantially accelerates farthest point sampling and neighbor search operations while preserving sampling quality and model performance. By integrating FastPoint into state-of-the-art 3D point cloud models, we achieve 2.55x end-to-end speedup on NVIDIA RTX 3090 GPU without sacrificing accuracy.

Adversarial attack plays a critical role in evaluating the robustness of deep learning models. Jacobian-based Saliency Map Attack (JSMA) is an interpretable adversarial method that offers excellent pixel-level control and provides valuable insights into model vulnerabilities. However, its quadratic computational complexity O(M^2 xN) renders it impractical for large-scale datasets, limiting its application despite its inherent value. This paper proposes FastJSMA, an efficient attack method that addresses these computational limitations. Our approach introduces a gradient decoupling mechanism that decomposes the Jacobian calculation into complementary class suppression (g^-) and class excitation (g^+) gradients, reducing complexity to O(M\sqrt N ). Additionally, we implement a class probing mechanism and an adaptive saliency threshold to further optimize the process. Experimental results across multiple datasets demonstrate that FastJSMA maintains comparable attack success rates while dramatically reducing computation time--requiring only 2.9% of JSMA's processing time on CIFAR-10 and 1.2% on CIFAR-100, and successfully operating on ImageNet where traditional JSMA fails due to memory constraints. This advancement enables the practical application of interpretable saliency map-based attacks on large-scale datasets, balancing effectiveness with computational efficiency.