arXiv subjects

Tan Chen

Publications and source records attributed to Tan Chen.

At least 19 recordsLinked to original sources

arXiv2026

FedPGT: Progressive Gradient Transmission for Vehicular Federated Learning over Time-Varying Channels

Vehicular federated learning (VFL) enables privacy-preserving collaborative model training for intelligent transportation systems, where communication resource allocation and gradient sparsification techniques have been explored to reduce communication overhead. However, vehicle mobility leads to rapidly varying channel conditions and transmission capacity, rendering predetermined resource allocation and sparsification decisions ineffective. In this paper, we propose FedPGT, a progressive gradient transmission scheme for VFL over time-varying channels, where vehicles progressively transmit high-magnitude gradient entries in response to instantaneous channel conditions. We establish a convergence bound that characterizes the impact of transmitted gradient entries and reveals diminishing-return behavior governed by a power-law decay. Motivated by this result, we formulate a stochastic optimization problem for online decision-making, where the main challenge lies in a cumulatively coupled, non-separable objective. To handle this challenge, we introduce per-slot surrogate transmission variables to decouple the long-term dependence across time slots and convert the original objective into an additive per-slot optimization problem, enabling a Lyapunov drift-plus-penalty approach for online scheduling. We further develop a low-complexity resource allocation algorithm for efficient online implementation. Experimental results demonstrate that the proposed scheme achieves a 3.65% accuracy improvement on the CIFAR-10 image classification task and a 12.66% reduction in average displacement error on the Argoverse trajectory prediction task compared with state-of-the-art baselines, demonstrating its applicability to diverse learning tasks under highly dynamic vehicular environments.

cs.LG↗

arXiv2026

Sylvas: Synergistic Learning Value based Device Scheduling in Federated Continual Learning

Federated continual learning (FCL) enables shared global models to continuously adapt to distributed and non-stationary data streams, making it important for Internet of Things applications such as intelligent transportation, industrial monitoring, and unmanned systems. Under spatio-temporal data distribution dynamics and label scarcity, a key challenge is how to quantify the contribution of each edge device to global learning performance and schedule the most valuable devices under resource constraints for timely model updating. This article presents Sylvas, a synergistic learning value based device scheduling framework for FCL at the wireless edge. Sylvas evaluates the learning value of distributed data from two perspectives: distributional value, which characterizes the contribution of device data to global model learning from a spatio-temporal distribution perspective, and label value, which captures the quantity and reliability tradeoff of pseudo-labeled data. By integrating these factors into a synergistic learning value metric, Sylvas schedules devices with high learning value while satisfying communication and computation resource constraints. Case studies demonstrate that Sylvas supports timely model adaptation under spatio-temporal distribution dynamics and effectively exploits unlabeled data.

cs.LG↗

arXiv2026

Periodic Gravitational Lensing with Oscillating Boson Stars

We show that oscillating (real-scalar) boson stars generically host an \emph{oscillating radial caustic}. Sources near this caustic cross it every half period, thereby producing periodic caustic-crossing lensing. The resulting observables are phase locked to the lens oscillation: image-pair creation or annihilation, changing image morphology, achromatic photometric spikes, and astrometric motion. This signal provides a distinctive target for time-domain astronomy, and its detection would reveal an intrinsically time-dependent compact dark-sector object. Event-number estimates indicate a measurable discovery space with current astrometric and high-cadence photometric surveys, while null searches would constrain the abundance of such objects as dark matter. The predictions rely only on the dynamics of real-scalar condensates and extend naturally to self-interacting real scalars, including axionlike particles, and to ultralight vector bosons.

gr-qc↗

arXiv2026

Spherically Symmetric Fluid Simulations of Black Hole Accretion in Self-Interacting Dark Matter Halos

We investigate black hole accretion in self-interacting dark matter (SIDM) halos using a self-gravitating fluid model with thermal conduction. We develop a robust one-dimensional spherically symmetric hydrodynamic code based on an operator-splitting finite-volume method. Simulating both Singular Isothermal Sphere (SIS) and Navarro-Frenk-White (NFW) profiles, we find that black hole growth is regulated by the competition between gravity-driven inflow and SIDM heat transport. Our results demonstrate that an SIS-like environment facilitates rapid accretion, allowing a $100\,\mathrm{M_{\odot}}$ seed to grow to $10^4\,\mathrm{M_{\odot}}$ within $2\,\mathrm{Myr}$. Furthermore, we show that larger initial black hole masses, steeper density profiles, and higher scattering cross sections significantly enhance the accretion rate. This study provides a comprehensive fluid-dynamical picture of black hole growth in SIDM halos.

astro-ph.CO↗

arXiv2025

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning

Federated edge learning (FEEL) enables collaborative model training across distributed clients over wireless networks without exposing raw data. While most existing studies assume static datasets, in real-world scenarios clients may continuously collect data with time-varying and non-independent and identically distributed (non-i.i.d.) characteristics. A critical challenge is how to adapt models in a timely yet efficient manner to such evolving data. In this paper, we propose FedTeddi, a temporal-drift-and-divergence-aware scheduling algorithm that facilitates fast convergence of FEEL under dynamic data evolution and communication resource limits. We first quantify the temporal dynamics and non-i.i.d. characteristics of data using temporal drift and collective divergence, respectively, and represent them as the Earth Mover's Distance (EMD) of class distributions for classification tasks. We then propose a novel optimization objective and develop a joint scheduling and bandwidth allocation algorithm, enabling the FEEL system to learn from new data quickly without forgetting previous knowledge. Experimental results show that our algorithm achieves higher test accuracy and faster convergence compared to benchmark methods, improving the rate of convergence by 58.4% on CIFAR-10 and 49.2% on CIFAR-100 compared to random scheduling.

cs.LG↗

arXiv2025

Dynamic Scheduling for Vehicle-to-Vehicle Communications Enhanced Federated Learning

Leveraging the computing and sensing capabilities of vehicles, vehicular federated learning (VFL) has been applied to edge training for connected vehicles. The dynamic and interconnected nature of vehicular networks presents unique opportunities to harness direct vehicle-to-vehicle (V2V) communications, enhancing VFL training efficiency. In this paper, we formulate a stochastic optimization problem to optimize the VFL training performance, considering the energy constraints and mobility of vehicles, and propose a V2V-enhanced dynamic scheduling (VEDS) algorithm to solve it. The model aggregation requirements of VFL and the limited transmission time due to mobility result in a stepwise objective function, which presents challenges in solving the problem. We thus propose a derivative-based drift-plus-penalty method to convert the long-term stochastic optimization problem to an online mixed integer nonlinear programming (MINLP) problem, and provide a theoretical analysis to bound the performance gap between the online solution and the offline optimal solution. Further analysis of the scheduling priority reduces the original problem into a set of convex optimization problems, which are efficiently solved using the interior-point method. Experimental results demonstrate that compared with the state-of-the-art benchmarks, the proposed algorithm enhances the image classification accuracy on the CIFAR-10 dataset by 4.20% and reduces the average displacement errors on the Argoverse trajectory prediction dataset by 9.82%.

cs.LG↗

arXiv2025

FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning

Federated learning (FL) is a promising paradigm for multiple devices to cooperatively train a model. When applied in wireless networks, two issues consistently affect the performance of FL, i.e., data heterogeneity of devices and limited bandwidth. Many papers have investigated device scheduling strategies considering the two issues. However, most of them recognize data heterogeneity as a property of individual devices. In this paper, we prove that the convergence speed of FL is affected by the sum of device-level and sample-level collective gradient divergence (CGD). The device-level CGD refers to the gradient divergence of the scheduled device group, instead of the sum of the individual device divergence. The sample-level CGD is statistically upper bounded by sampling variance, which is inversely proportional to the total number of samples scheduled for local update. To derive a tractable form of the device-level CGD, we further consider a classification problem and transform it into the weighted earth moving distance (WEMD) between the group distribution and the global distribution. Then we propose FedCGD algorithm to minimize the sum of multi-level CGDs by balancing WEMD and sampling variance, within polynomial time. Simulation shows that the proposed strategy increases classification accuracy on the CIFAR-10 dataset by up to 4.2\% while scheduling 41.8\% fewer devices, and flexibly switches between reducing WEMD and reducing sampling variance.

cs.LG↗

arXiv2025

Mobility-Aware Asynchronous Federated Learning with Dynamic Sparsification

Asynchronous Federated Learning (AFL) enables distributed model training across multiple mobile devices, allowing each device to independently update its local model without waiting for others. However, device mobility introduces intermittent connectivity, which necessitates gradient sparsification and leads to model staleness, jointly affecting AFL convergence. This paper develops a theoretical model to characterize the interplay among sparsification, model staleness and mobility-induced contact patterns, and their joint impact on AFL convergence. Based on the analysis, we propose a mobility-aware dynamic sparsification (MADS) algorithm that optimizes the sparsification degree based on contact time and model staleness. Closed-form solutions are derived, showing that under low-speed conditions, MADS increases the sparsification degree to enhance convergence, while under high-speed conditions, it reduces the sparsification degree to guarantee reliable uploads within limited contact time. Experimental results validate the theoretical findings. Compared with the state-of-the-art benchmarks, the MADS algorithm increases the image classification accuracy on the CIFAR-10 dataset by 8.76% and reduces the average displacement error in the Argoverse trajectory prediction dataset by 9.46%.

cs.LG↗

arXiv2025

Modulations of Gravitational Waves due to Non-static Gravitational Lenses

Gravitational waves (GWs) offer a new observational window into the universe, providing insights into compact objects and cosmic structures. Gravitational lensing, commonly studied in electromagnetic waves, also affects GWs, introducing magnification, time delays, and multiple images. While existing studies focus on static lenses, many astrophysical lenses are dynamic, with time-varying mass distributions such as moving stars or orbiting binaries. We develop a general theoretical framework to describe non-static lenses and demonstrate how they modulate GW signals, inducing unique time-varying amplitude modulations and spectral broadening. By examining uniformly moving and orbiting binary lenses, we show that these modulations provide new observational signatures, enhancing our understanding of lensing objects and the dynamics of the universe. Our findings have important implications for GW astronomy, offering novel ways to probe lens dynamics and improve the interpretations of GW signals.

astro-ph.CO↗

arXiv2025

Benchmarking Model Predictive Control and Reinforcement Learning Based Control for Legged Robot Locomotion in MuJoCo Simulation

Model Predictive Control (MPC) and Reinforcement Learning (RL) are two prominent strategies for controlling legged robots, each with unique strengths. RL learns control policies through system interaction, adapting to various scenarios, whereas MPC relies on a predefined mathematical model to solve optimization problems in real-time. Despite their widespread use, there is a lack of direct comparative analysis under standardized conditions. This work addresses this gap by benchmarking MPC and RL controllers on a Unitree Go1 quadruped robot within the MuJoCo simulation environment, focusing on a standardized task-straight walking at a constant velocity. Performance is evaluated based on disturbance rejection, energy efficiency, and terrain adaptability. The results show that RL excels in handling disturbances and maintaining energy efficiency but struggles with generalization to new terrains due to its dependence on learned policies tailored to specific environments. In contrast, MPC shows enhanced recovery capabilities from larger perturbations by leveraging its optimization-based approach, allowing for a balanced distribution of control efforts across the robot's joints. The results provide a clear understanding of the advantages and limitations of both RL and MPC, offering insights into selecting an appropriate control strategy for legged robotic applications.

cs.RO↗

arXiv2025

OpenHumanVid: A Large-Scale High-Quality Dataset for Enhancing Human-Centric Video Generation

Recent advancements in visual generation technologies have markedly increased the scale and availability of video datasets, which are crucial for training effective video generation models. However, a significant lack of high-quality, human-centric video datasets presents a challenge to progress in this field. To bridge this gap, we introduce OpenHumanVid, a large-scale and high-quality human-centric video dataset characterized by precise and detailed captions that encompass both human appearance and motion states, along with supplementary human motion conditions, including skeleton sequences and speech audio. To validate the efficacy of this dataset and the associated training strategies, we propose an extension of existing classical diffusion transformer architectures and conduct further pretraining of our models on the proposed dataset. Our findings yield two critical insights: First, the incorporation of a large-scale, high-quality dataset substantially enhances evaluation metrics for generated human videos while preserving performance in general video generation tasks. Second, the effective alignment of text with human appearance, human motion, and facial motion is essential for producing high-quality video outputs. Based on these insights and corresponding methodologies, the straightforward extended network trained on the proposed dataset demonstrates an obvious improvement in the generation of human-centric videos. Project page https://fudan-generative-vision.github.io/OpenHumanVid

cs.CV↗

arXiv2024

Quasinormal modes of gravitational perturbation for uniformly accelerated black holes

We first show that the master equations for massless perturbations of accelerating rotating black holes can be transformed into the Heun's equation. The quasinormal modes of the black holes can be easily calculated in the framework of the Heun's equation. We identify three modes for the tensor perturbations: the photon sphere modes, which reduce to the quasinormal modes of Kerr black holes when the acceleration parameter vanishes; the near-extremal modes, which branch from the first set and become dominant when the spin is near extremal; and the acceleration modes, which are closely related to the acceleration horizon. We calculate the frequency spectrum of the QNMs in various spin and acceleration parameters. We choose an angular boundary condition that keeps the angular function regular at $ θ= 0 $ and $π$, which is consistent with the boundary condition of the Kerr black hole. The conical singularity caused by the acceleration influences this boundary condition. We find that the $m_0 = 1$ modes have an anomalous behavior at particular accelerations.

gr-qc↗

arXiv2024

Mobility Accelerates Learning: Convergence Analysis on Hierarchical Federated Learning in Vehicular Networks

Hierarchical federated learning (HFL) enables distributed training of models across multiple devices with the help of several edge servers and a cloud edge server in a privacy-preserving manner. In this paper, we consider HFL with highly mobile devices, mainly targeting at vehicular networks. Through convergence analysis, we show that mobility influences the convergence speed by both fusing the edge data and shuffling the edge models. While mobility is usually considered as a challenge from the perspective of communication, we prove that it increases the convergence speed of HFL with edge-level heterogeneous data, since more diverse data can be incorporated. Furthermore, we demonstrate that a higher speed leads to faster convergence, since it accelerates the fusion of data. Simulation results show that mobility increases the model accuracy of HFL by up to 15.1% when training a convolutional neural network on the CIFAR-10 dataset.

cs.LG↗

arXiv2023

The Impact of Time Step Frequency on the Realism of Robotic Manipulation Simulation for Objects of Different Scales

This work evaluates the impact of time step frequency and component scale on robotic manipulation simulation accuracy. Increasing the time step frequency for small-scale objects is shown to improve simulation accuracy. This simulation, demonstrating pre-assembly part picking for two object geometries, serves as a starting point for discussing how to improve Sim2Real transfer in robotic assembly processes.

cs.RO↗

arXiv2023

Hierarchical Intention Tracking for Robust Human-Robot Collaboration in Industrial Assembly Tasks

Collaborative robots require effective human intention estimation to safely and smoothly work with humans in less structured tasks such as industrial assembly, where human intention continuously changes. We propose the concept of intention tracking and introduce a collaborative robot system that concurrently tracks intentions at hierarchical levels. The high-level intention is tracked to estimate human's interaction pattern and enable robot to (1) avoid collision with human to minimize interruption and (2) assist human to correct failure. The low-level intention estimate provides robot with task-related information. We implement the system on a UR5e robot and demonstrate robust, seamless and ergonomic human-robot collaboration in an ablative pilot study of an assembly use case. Our robot demonstrations and videos are available at \url{https://sites.google.com/view/hierarchicalintentiontracking}.

cs.RO↗

arXiv2023

Data-Heterogeneous Hierarchical Federated Learning with Mobility

Federated learning enables distributed training of machine learning (ML) models across multiple devices in a privacy-preserving manner. Hierarchical federated learning (HFL) is further proposed to meet the requirements of both latency and coverage. In this paper, we consider a data-heterogeneous HFL scenario with mobility, mainly targeting vehicular networks. We derive the convergence upper bound of HFL with respect to mobility and data heterogeneity, and analyze how mobility impacts the performance of HFL. While mobility is considered as a challenge from a communication point of view, our goal here is to exploit mobility to improve the learning performance by mitigating data heterogeneity. Simulation results verify the analysis and show that mobility can indeed improve the model accuracy by up to 15.1\% when training a convolutional neural network on the CIFAR-10 dataset using HFL.

cs.LG↗

arXiv2023

Hypergraph-based Multi-Robot Task and Motion Planning

We present a multi-robot task and motion planning method that, when applied to the rearrangement of objects by manipulators, results in solution times up to three orders of magnitude faster than existing methods and successfully plans for problems with up to twenty objects, more than three times as many objects as comparable methods. We achieve this improvement by decomposing the planning space to consider manipulators alone, objects, and manipulators holding objects. We represent this decomposition with a hypergraph where vertices are decomposed elements of the planning spaces and hyperarcs are transitions between elements. Existing methods use graph-based representations where vertices are full composite spaces and edges are transitions between these. Using the hypergraph reduces the representation size of the planning space-for multi-manipulator object rearrangement, the number of hypergraph vertices scales linearly with the number of either robots or objects, while the number of hyperarcs scales quadratically with the number of robots and linearly with the number of objects. In contrast, the number of vertices and edges in graph-based representations scales exponentially in the number of robots and objects. We show that similar gains can be achieved for other multi-robot task and motion planning problems.

cs.RO↗

arXiv2023

Gravitational microlensing by dressed primordial black holes

The accretion of dark matter around the primordial black holes (PBHs) could lead to the formation of surrounding minihalos, whose mass can be several orders of magnitude higher than the central PBH mass. The gravitational microlensing produced by such dressed PBHs could be quite different from that of the bare PBHs, which may significantly affect the constraints on the PBH abundance. In this paper, we study the gravitational microlensing produced by dressed PBHs in detail. We find that all the microlensing effects by dressed PBHs have asymptotic behavior depending on the minihalo size, which can be used to predict the microlensing effects by comparing the halo size with the Einstein radius. When the minihalo radius and the Einstein radius are comparable, the effect of the density distribution of the halo is significant to the microlensing. Applying the stellar microlensing by dressed PBHs to the data of the Optical Gravitational Lensing Experiment and Subaru/HSC Andromeda observations, we obtain the improved constraints on the PBH abundance. It shows that the existence of dark matter minihalos surrounding PBHs can strengthen the constraints on the PBH abundance from stellar microlensing by several orders, and can shift the constraints to the well-known asteroid mass window where PBHs can constitute all the dark matter.

astro-ph.CO↗