Temporal point processes (TPPs) are effective for modeling event occurrences over time but struggle with sparse and uncertain events in federated systems, where privacy is a major concern. To address this, we propose FedPP , a federated neural nonparametric point process model. FedPP integrates neural embeddings into sigmoidal Gaussian Cox processes (SGCPs) on the client side. SGCPs is a flexible and expressive class of TPPs, allowing FedPP to generate highly flexible intensity functions that capture client-specific event dynamics and uncertainties while efficiently summarizing historical records. For global aggregation, FedPP introduces a divergence-based mechanism to communicate the distributions of kernel hyperparameters in SGCPs between the server and clients, while keeping client-specific parameters local to ensure privacy and personalization. FedPP effectively captures event uncertainty and sparsity. Extensive experiments demonstrate its superior performance in federated settings, showing global aggregation with the KL divergence and the Wasserstein distance.

Abstract Bayesian optimization is a powerful technique for optimizing expensive-to-evaluate black-box functions, consisting of two main components: a surrogate model and an acquisition function. In recent years, myopic acquisition functions have been widely adopted for their simplicity and effectiveness. However, their lack of look-ahead capability limits their performance. To address this limitation, we propose FigBO, a generalized acquisition function that incorporates the future impact of candidate points on global information gain. FigBO is a plug-and-play method that can integrate seamlessly with most existing myopic acquisition functions. Theoretically, we analyze the regret bound and convergence rate of FigBO when combined with the myopic base acquisition function expected improvement (EI), comparing them to those of standard EI. Empirically, extensive experimental results across diverse tasks demonstrate that FigBO achieves state-of-the-art performance and significantly faster convergence compared to existing methods.

• Design a self-attention aggregation module using a pre-trained diffusion model. • Integrate high-precision masks into RGB-D SLAM for robust dynamic tracking. • Validate superior accuracy and efficiency on dynamic simulation datasets. In dynamic environments, performing RGB-D SLAM (Simultaneous Localization and Mapping) faces significant challenges primarily due to the presence of moving objects. The motion of these objects can introduce tracking errors and inaccuracies in map construction, thereby compromising the stability and overall performance of the system. To maintain high-precision localization and mapping under such conditions, a SLAM system must effectively detect and handle dynamic objects. To address these challenges, this paper presents a novel RGB-D SLAM method, referred to as DMSAA-SLAM (Dynamic Scene SLAM Based on Diffusion Model Self-Attention Aggregation). The core idea is to leverage a pre-trained stable diffusion model, particularly its self-attention layers, to handle the complexity of dynamic scenes. By employing a multi-resolution aggregation approach, combined with iterative merging and nonmaximum suppression, the proposed method generates high-precision segmentation masks. These masks enable fine-grained segmentation of moving objects and effectively eliminate dynamic feature points, thereby mitigating the impact of dynamic elements on the SLAM process and ensuring efficient and accurate tracking and mapping.

Personalized federated learning (PFL) is a promising technique for tackling data heterogeneity in federated learning systems. Recently, Bayesian neural networks (BNNs) have been introduced into the PFL framework to enable uncertainty quantification and improve performance in data-scarce settings. Despite these advantages, existing BNN-based PFL methods face two key challenges in practical applications. First, in real-world scenarios, client heterogeneity often arises in the form of group-wise variation, which cannot be adequately captured by a single shared distribution as assumed in prior work. Second, existing methods rely on deterministic or stochastic approximation techniques for posterior inference, which lead to substantial computational and memory overhead, hindering their scalability and deployment. To address these limitations, we propose DBFed, a novel BNN-based PFL framework from a distribution-based perspective. DBFed introduces group-specific distributions to better model the structural heterogeneity commonly observed in federated settings. Moreover, DBFed employs a rank-1 parameterization technique to map uncertainty from the weight space to a low-dimensional subspace, significantly reducing the computational and memory overhead. Theoretically, we establish the effectiveness of the rank-1 parameterization approach. Empirically, extensive experiments on diverse datasets demonstrate that DBFed consistently outperforms alternative PFL baselines in a heterogeneous setting.

Personalized affective dialogue systems are critical for mental health applications, where responses must be emotionally appropriate and tailored to individual users. However, most existing large language model (LLM) based approaches rely on deterministic personalization, ignore uncertainty in affective understanding, and are difficult to deploy under privacy constraints. In this paper, we propose PALLM, a personalized affective large language modeling framework designed for privacy-preserving mental health dialogue. PALLM decouples personalization into two complementary components: a deterministic personalization layer that captures stable user preferences, and a Bayesian affective representation layer that models dynamic emotional states and uncertainty. By restricting uncertainty modeling to affective representations rather than full LLM parameters, PALLM achieves efficient and scalable personalization under federated learning. Extensive experiments on EmpatheticDialogues and a real-world mental health conversation dataset show that PALLM improves affective alignment and robustness compared with non-personalized and partially personalized baselines.

Federated learning has emerged as an effective paradigm for training machine learning models across distributed clients without sharing raw data. In many real-world applications, sequential data are inherently multimodal, involving heterogeneous streams such as audio, visual, and temporal signals. However, most existing federated approaches rely on deterministic neural networks, which often struggle to capture predictive uncertainty under heterogeneous data distributions, cross-modal inconsistencies, and dynamic client participation. In this paper, we propose FedBayesMamba , a Bayesian federated learning framework for multimodal sequential data modeling based on selective state space models. The proposed approach introduces Bayesian parameterization into the Mamba architecture to enable uncertainty-aware sequence modeling while preserving the computational efficiency of state space models. To effectively integrate uncertainty across distributed clients, we further develop a posterior aggregation strategy that combines client-level posterior distributions in a principled probabilistic manner. Extensive experiments on multiple benchmark datasets demonstrate that the proposed framework achieves competitive predictive performance and improved uncertainty estimation under Non-IID federated settings. The results also indicate that FedBayesMamba exhibits strong robustness and stability in challenging federated scenarios. These findings highlight the potential of combining Bayesian learning with state space models for multimodal temporal modeling, particularly in audio-visual perception and cross-modal sequence understanding tasks.

The recently proposed Bayesian Flow Networks (BFNs) show great potential in modeling parameter spaces via a diffusion process, offering a unified strategy for handling continuous, discrete data. However, these parameter diffusion models cannot learn high-level semantic representation from the parameter space since common encoders, which encode data into one static representation, can- not capture semantic changes in parameters. This motivates a new direction: learning semantic representations hidden in the param- eter spaces to characterize noisy data. Accordingly, we propose a representation learning framework named SepDiff which operates in the parameter space to obtain parameter-wise latent semantics that exhibit progressive structures. Specifically, SepDiff proposes a self-encoder to learn latent semantics directly from parameters, rather than from observations. The encoder is then integrated into parameter diffusion model, enabling representation learning with various formats of observations. Mutual information terms further promote the disentanglement of latent semantics and capture mean- ingful semantics simultaneously. We illustrate seven representation learning tasks in SepDiff via expanding this parameter diffusion model, and extensive quantitative experimental results demonstrate the superior effectiveness of SepDiff in learning parameter repre- sentation.

While deep neural networks (DNNs)-based personalized federated learning (PFL) is demanding for addressing data heterogeneity and shows promising performance, existing methods for federated learning (FL) suffer from efficient systematic uncertainty quantification. The Bayesian DNNs-based PFL is usually questioned of either oversimplified model structures or high computational and memory costs. In this article, we introduce FedSI, a novel Bayesian DNNs-based subnetwork inference (SI) PFL framework. FedSI is simple and scalable by leveraging Bayesian methods to incorporate systematic uncertainties effectively. It implements a client-specific SI mechanism, selects network parameters with large variance to be inferred through posterior distributions, and fixes the rest as deterministic ones. FedSI achieves fast and scalable inference while preserving the systematic uncertainties to the fullest extent. Extensive experiments on four different benchmark datasets demonstrate that FedSI outperforms existing Bayesian and non-Bayesian FL baselines in heterogeneous FL scenarios.