arXiv subjects

Song Guo

Publications and source records attributed to Song Guo.

At least 91 records · Page 5Linked to original sources

arXiv2025

Epass: Efficient and Privacy-Preserving Asynchronous Payment on Blockchain

Buy Now Pay Later (BNPL) is a rapidly proliferating e-commerce model, offering consumers to get the product immediately and defer payments. Meanwhile, emerging blockchain technologies endow BNPL platforms with digital currency transactions, allowing BNPL platforms to integrate with digital wallets. However, the transparency of transactions causes critical privacy concerns because malicious participants may derive consumers' financial statuses from on-chain asynchronous payments. Furthermore, the newly created transactions for deferred payments introduce additional time overheads, which weaken the scalability of BNPL services. To address these issues, we propose an efficient and privacy-preserving blockchain-based asynchronous payment scheme (Epass), which has promising scalability while protecting the privacy of on-chain consumer transactions. Specifically, Epass leverages locally verifiable signatures to guarantee the privacy of consumer transactions against malicious acts. Then, a privacy-preserving asynchronous payment scheme can be further constructed by leveraging time-release encryption to control trapdoors of redactable blockchain, reducing time overheads by modifying transactions for deferred payment. We give formal definitions and security models, generic structures, and formal proofs for Epass. Extensive comparisons and experimental analysis show that \textsf{Epass} achieves KB-level communication costs, and reduces time overhead by more than four times in comparisons with locally verifiable signatures and Go-Ethereum private test networks.

cs.CR↗

arXiv2025

Facial Expression Analysis and Its Potentials in IoT Systems: A Contemporary Survey

Facial expressions convey human emotions and can be categorized into macro-expressions (MaEs) and micro-expressions (MiEs) based on duration and intensity. While MaEs are voluntary and easily recognized, MiEs are involuntary, rapid, and can reveal concealed emotions. The integration of facial expression analysis with Internet-of-Thing (IoT) systems has significant potential across diverse scenarios. IoT-enhanced MaE analysis enables real-time monitoring of patient emotions, facilitating improved mental health care in smart healthcare. Similarly, IoT-based MiE detection enhances surveillance accuracy and threat detection in smart security. Our work aims to provide a comprehensive overview of research progress in facial expression analysis and explores its potential integration with IoT systems. We discuss the distinctions between our work and existing surveys, elaborate on advancements in MaE and MiE analysis techniques across various learning paradigms, and examine their potential applications in IoT. We highlight challenges and future directions for the convergence of facial expression-based technologies and IoT systems, aiming to foster innovation in this domain. By presenting recent developments and practical applications, our work offers a systematic understanding of the ways of facial expression analysis to enhance IoT systems in healthcare, security, and beyond.

cs.AI↗

arXiv2025

FreeMesh: Boosting Mesh Generation with Coordinates Merging

The next-coordinate prediction paradigm has emerged as the de facto standard in current auto-regressive mesh generation methods. Despite their effectiveness, there is no efficient measurement for the various tokenizers that serialize meshes into sequences. In this paper, we introduce a new metric Per-Token-Mesh-Entropy (PTME) to evaluate the existing mesh tokenizers theoretically without any training. Building upon PTME, we propose a plug-and-play tokenization technique called coordinate merging. It further improves the compression ratios of existing tokenizers by rearranging and merging the most frequent patterns of coordinates. Through experiments on various tokenization methods like MeshXL, MeshAnything V2, and Edgerunner, we further validate the performance of our method. We hope that the proposed PTME and coordinate merging can enhance the existing mesh tokenizers and guide the further development of native mesh generation.

cs.GR↗

arXiv2025

Unleashing the Power of Continual Learning on Non-Centralized Devices: A Survey

Non-Centralized Continual Learning (NCCL) has become an emerging paradigm for enabling distributed devices such as vehicles and servers to handle streaming data from a joint non-stationary environment. To achieve high reliability and scalability in deploying this paradigm in distributed systems, it is essential to conquer challenges stemming from both spatial and temporal dimensions, manifesting as distribution shifts, catastrophic forgetting, heterogeneity, and privacy issues. This survey focuses on a comprehensive examination of the development of the non-centralized continual learning algorithms and the real-world deployment across distributed devices. We begin with an introduction to the background and fundamentals of non-centralized learning and continual learning. Then, we review existing solutions from three levels to represent how existing techniques alleviate the catastrophic forgetting and distribution shift. Additionally, we delve into the various types of heterogeneity issues, security, and privacy attributes, as well as real-world applications across three prevalent scenarios. Furthermore, we establish a large-scale benchmark to revisit this problem and analyze the performance of the state-of-the-art NCCL approaches. Finally, we discuss the important challenges and future research directions in NCCL.

cs.LG↗

arXiv2025

D$^{2}$MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving

The mixture of experts (MoE) model is a sparse variant of large language models (LLMs), designed to hold a better balance between intelligent capability and computational overhead. Despite its benefits, MoE is still too expensive to deploy on resource-constrained edge devices, especially with the demands of on-device inference services. Recent research efforts often apply model compression techniques, such as quantization, pruning and merging, to restrict MoE complexity. Unfortunately, due to their predefined static model optimization strategies, they cannot always achieve the desired quality-overhead trade-off when handling multiple requests, finally degrading the on-device quality of service. These limitations motivate us to propose the D$^2$MoE, an algorithm-system co-design framework that matches diverse task requirements by dynamically allocating the most proper bit-width to each expert. Specifically, inspired by the nested structure of matryoshka dolls, we propose the matryoshka weight quantization (MWQ) to progressively compress expert weights in a bit-nested manner and reduce the required runtime memory. On top of it, we further optimize the I/O-computation pipeline and design a heuristic scheduling algorithm following our hottest-expert-bit-first (HEBF) principle, which maximizes the expert parallelism between I/O and computation queue under constrained memory budgets, thus significantly reducing the idle temporal bubbles waiting for the experts to load. Evaluations on real edge devices show that D$^2$MoE improves the overall inference throughput by up to 1.39$\times$ and reduces the peak memory footprint by up to 53% over the latest on-device inference frameworks, while still preserving comparable serving accuracy as its INT8 counterparts.

cs.DC↗

arXiv2025

RORem: Training a Robust Object Remover with Human-in-the-Loop

Despite the significant advancements, existing object removal methods struggle with incomplete removal, incorrect content synthesis and blurry synthesized regions, resulting in low success rates. Such issues are mainly caused by the lack of high-quality paired training data, as well as the self-supervised training paradigm adopted in these methods, which forces the model to in-paint the masked regions, leading to ambiguity between synthesizing the masked objects and restoring the background. To address these issues, we propose a semi-supervised learning strategy with human-in-the-loop to create high-quality paired training data, aiming to train a Robust Object Remover (RORem). We first collect 60K training pairs from open-source datasets to train an initial object removal model for generating removal samples, and then utilize human feedback to select a set of high-quality object removal pairs, with which we train a discriminator to automate the following training data generation process. By iterating this process for several rounds, we finally obtain a substantial object removal dataset with over 200K pairs. Fine-tuning the pre-trained stable diffusion model with this dataset, we obtain our RORem, which demonstrates state-of-the-art object removal performance in terms of both reliability and image quality. Particularly, RORem improves the object removal success rate over previous methods by more than 18\%. The dataset, source code and trained model are available at https://github.com/leeruibin/RORem.

cs.CV↗

arXiv2025

Spider: Any-to-Many Multimodal LLM

Multimodal LLMs (MLLMs) have emerged as an extension of Large Language Models (LLMs), enabling the integration of various modalities. However, Any-to-Any MLLMs are limited to generating pairwise modalities 'Text + X' within a single response, such as Text + {Image or Audio or Video}. To address this limitation, we introduce Spider, a novel efficient Any-to-Many Modalities Generation (AMMG) framework, which can generate an arbitrary combination of modalities 'Text + Xs', such as Text + {Image and Audio and Video}. To achieve efficient AMMG, our Spider integrates three core components: a Base Model for basic X-to-X (i.e., Any-to-Any) modality processing, an Any-to-Many Instruction Template designed for producing Xs signal prompts, and a novel Efficient Decoders-Controller for controlling multimodal Decoders to generate Xs (many-modal) contents. To train Spider, we constructed a novel Text-formatted Many-Modal (TMM) dataset, which facilitates learning the X-to-Xs (i.e., Any-to-Many) capability necessary for AMMG. Ultimately, the well-trained Spider generates a pseudo X-to-Xs dataset, the first-ever X-to-Xs many-modal dataset, enhancing the potential for AMMG tasks in future research. Overall, this work not only pushes the boundary of multimodal interaction but also provides rich data support for advancing the field. Code: https://github.com/Layjins/Spider

cs.CV↗

arXiv2025

BoxSeg: Quality-Aware and Peer-Assisted Learning for Box-supervised Instance Segmentation

Box-supervised instance segmentation methods aim to achieve instance segmentation with only box annotations. Recent methods have demonstrated the effectiveness of acquiring high-quality pseudo masks under the teacher-student framework. Building upon this foundation, we propose a BoxSeg framework involving two novel and general modules named the Quality-Aware Module (QAM) and the Peer-assisted Copy-paste (PC). The QAM obtains high-quality pseudo masks and better measures the mask quality to help reduce the effect of noisy masks, by leveraging the quality-aware multi-mask complementation mechanism. The PC imitates Peer-Assisted Learning to further improve the quality of the low-quality masks with the guidance of the obtained high-quality pseudo masks. Theoretical and experimental analyses demonstrate the proposed QAM and PC are effective. Extensive experimental results show the superiority of our BoxSeg over the state-of-the-art methods, and illustrate the QAM and PC can be applied to improve other models.

cs.CV↗

arXiv2025

Exploring Prosocial Irrationality for LLM Agents: A Social Cognition View

Large language models (LLMs) have been shown to face hallucination issues due to the data they trained on often containing human bias; whether this is reflected in the decision-making process of LLM Agents remains under-explored. As LLM Agents are increasingly employed in intricate social environments, a pressing and natural question emerges: Can we utilize LLM Agents' systematic hallucinations to mirror human cognitive biases, thus exhibiting irrational social intelligence? In this paper, we probe the irrational behavior among contemporary LLM Agents by melding practical social science experiments with theoretical insights. Specifically, We propose CogMir, an open-ended Multi-LLM Agents framework that utilizes hallucination properties to assess and enhance LLM Agents' social intelligence through cognitive biases. Experimental results on CogMir subsets show that LLM Agents and humans exhibit high consistency in irrational and prosocial decision-making under uncertain conditions, underscoring the prosociality of LLM Agents as social entities and highlighting the significance of hallucination properties. Additionally, the CogMir framework demonstrates its potential as a valuable platform for encouraging more research into the social intelligence of LLM Agents.

cs.CY↗

arXiv2025

Coherent Video Inpainting Using Optical Flow-Guided Efficient Diffusion

The text-guided video inpainting technique has significantly improved the performance of content generation applications. A recent family for these improvements uses diffusion models, which have become essential for achieving high-quality video inpainting results, yet they still face performance bottlenecks in temporal consistency and computational efficiency. This motivates us to propose a new video inpainting framework using optical Flow-guided Efficient Diffusion (FloED) for higher video coherence. Specifically, FloED employs a dual-branch architecture, where the time-agnostic flow branch restores corrupted flow first, and the multi-scale flow adapters provide motion guidance to the main inpainting branch. Besides, a training-free latent interpolation method is proposed to accelerate the multi-step denoising process using flow warping. With the flow attention cache mechanism, FLoED efficiently reduces the computational cost of incorporating optical flow. Extensive experiments on background restoration and object removal tasks show that FloED outperforms state-of-the-art diffusion-based methods in both quality and efficiency. Our codes and models will be made publicly available.

cs.CV↗

arXiv2025

Efficient Multi-user Offloading of Personalized Diffusion Models: A DRL-Convex Hybrid Solution

With the impressive generative capabilities of diffusion models, personalized content synthesis has emerged as the most highly anticipated. However, the large model sizes and iterative nature of inference make it difficult to deploy personalized diffusion models broadly on local devices with varying computational power. To this end, we propose a novel framework for efficient multi-user offloading of personalized diffusion models, given a variable number of users, diverse user computational capabilities, and fluctuating available computational resources on the edge server. To enhance computational efficiency and reduce storage burden on edge servers, we first propose a tailored multi-user hybrid inference manner, where the inference process for each user is split into two phases with an optimizable split point. The initial phase of inference is processed on a cluster-wide model using batching techniques, generating low-level semantic information corresponding to each user's prompt. Then, the users employ their own personalized model to add further details in the later inference phase. Given the constraints on edge server computational resources and users' preferences for low latency and high accuracy, we model the joint optimization of each user's offloading request handling and split point as an extension of the Generalized Quadratic Assignment Problem (GQAP). Our objective is to maximize a comprehensive metric that accounts for both latency and accuracy across all users. To tackle this NP-hard problem, we transform the GQAP into an adaptive decision sequence, model it as a Markov decision process, and develop a hybrid solution combining deep reinforcement learning with convex optimization techniques. Simulation results validate the effectiveness of our framework, demonstrating superior optimality and low complexity compared to traditional methods.

cs.NI↗

arXiv2025

Orchestrating Joint Offloading and Scheduling for Low-Latency Edge SLAM

Visual Simultaneous Localization and Mapping (vSLAM) is a prevailing technology for many emerging robotic applications. Achieving real-time SLAM on mobile robotic systems with limited computational resources is challenging because the complexity of SLAM algorithms increases over time. This restriction can be lifted by offloading computations to edge servers, forming the emerging paradigm of edge-assisted SLAM. Nevertheless, the exogenous and stochastic input processes affect the dynamics of the edge-assisted SLAM system. Moreover, the requirements of clients on SLAM metrics change over time, exerting implicit and time-varying effects on the system. In this paper, we aim to push the limit beyond existing edge-assist SLAM by proposing a new architecture that can handle the input-driven processes and also satisfy clients' implicit and time-varying requirements. The key innovations of our work involve a regional feature prediction method for importance-aware local data processing, a configuration adaptation policy that integrates data compression/decompression and task offloading, and an input-dependent learning framework for task scheduling with constraint satisfaction. Extensive experiments prove that our architecture improves pose estimation accuracy and saves up to 47% of communication costs compared with a popular edge-assisted SLAM system, as well as effectively satisfies the clients' requirements.

cs.RO↗

arXiv2025

The MoE-Empowered Edge LLMs Deployment: Architecture, Challenges, and Opportunities

The powerfulness of LLMs indicates that deploying various LLMs with different scales and architectures on end, edge, and cloud to satisfy different requirements and adaptive heterogeneous hardware is the critical way to achieve ubiquitous intelligence for 6G. However, the massive parameter scale of LLMs poses significant challenges in deploying them on edge devices due to high computational and storage demands. Considering that the sparse activation in Mixture of Experts (MoE) is effective on scalable and dynamic allocation of computational and communications resources at the edge, this paper proposes a novel MoE-empowered collaborative deployment framework for edge LLMs, denoted as CoEL. This framework fully leverages the properties of MoE architecture and encompasses four key aspects: Perception, Deployment, Compression, and Updating. Edge servers broadcast their resource status and the specific resource requirements of LLMs to their neighbors. Then, utilizing this data, two sophisticated deployment strategies are proposed for satisfying varying model scales, ensuring that each model is deployed effectively. One for deploying LLMs on a single edge device through intra-device resource collaboration, and another for a distributed deployment across multiple edge devices via inter-device resource collaboration. Furthermore, both the models and the intermediate data are compressed for reducing memory footprint by quantization and reducing the volume of intermediate data by token fusion and pruning. Finally, given the dynamic of network topology, resource status, and user requirements, the deployment strategies are regularly updated to maintain its relevance and effectiveness. This paper also delineates the challenges and potential research directions for the deployment of edge LLMs.

cs.NI↗

arXiv2025

TsCA: On the Semantic Consistency Alignment via Conditional Transport for Compositional Zero-Shot Learning

Compositional Zero-Shot Learning (CZSL) aims to recognize novel state-object compositions by leveraging the shared knowledge of their primitive components. Despite considerable progress, effectively calibrating the bias between semantically similar multimodal representations, as well as generalizing pre-trained knowledge to novel compositional contexts, remains an enduring challenge. In this paper, our interest is to revisit the conditional transport (CT) theory and its homology to the visual-semantics interaction in CZSL and further, propose a novel Trisets Consistency Alignment framework (dubbed TsCA) that well-addresses these issues. Concretely, we utilize three distinct yet semantically homologous sets, i.e., patches, primitives, and compositions, to construct pairwise CT costs to minimize their semantic discrepancies. To further ensure the consistency transfer within these sets, we implement a cycle-consistency constraint that refines the learning by guaranteeing the feature consistency of the self-mapping during transport flow, regardless of modality. Moreover, we extend the CT plans to an open-world setting, which enables the model to effectively filter out unfeasible pairs, thereby speeding up the inference as well as increasing the accuracy. Extensive experiments are conducted to verify the effectiveness of the proposed method.

cs.CV↗

arXiv2025

6G Communication New Paradigm: The Integration of Unmanned Aerial Vehicles and Intelligent Reflecting Surfaces

With the continuous development of Intelligent Reflecting Surfaces (IRSs) and Unmanned Aerial Vehicles (UAVs), their combination has become foundational technologies to complement the terrestrial network by providing communication enhancement services for large-scale users. This article provides a comprehensive overview of IRS-assisted UAV communications for 6th-Generation (6G) networks. First, the applications supported by IRS-assisted UAV communications for 6G networks are introduced, and key issues originated from applications supported by IRSs and UAVs for 6G networks are summarized and analyzed. Then, prototypes and main technologies related to the integration of IRSs and UAVs are introduced. Driven by applications and technologies of IRS-assisted UAV communications, existing solutions in the realms of energy-constrained communications, secure communications, and enhanced communications are summarized, and corresponding empirical lessons are provided. Finally, we discuss some research challenges and open issues in IRS-assisted UAV communications, offering directions for the future development.

cs.NI↗

arXiv2025

Mjolnir: Breaking the Shield of Perturbation-Protected Gradients via Adaptive Diffusion

Perturbation-based mechanisms, such as differential privacy, mitigate gradient leakage attacks by introducing noise into the gradients, thereby preventing attackers from reconstructing clients' private data from the leaked gradients. However, can gradient perturbation protection mechanisms truly defend against all gradient leakage attacks? In this paper, we present the first attempt to break the shield of gradient perturbation protection in Federated Learning for the extraction of private information. We focus on common noise distributions, specifically Gaussian and Laplace, and apply our approach to DNN and CNN models. We introduce Mjolnir, a perturbation-resilient gradient leakage attack that is capable of removing perturbations from gradients without requiring additional access to the original model structure or external data. Specifically, we leverage the inherent diffusion properties of gradient perturbation protection to develop a novel diffusion-based gradient denoising model for Mjolnir. By constructing a surrogate client model that captures the structure of perturbed gradients, we obtain crucial gradient data for training the diffusion model. We further utilize the insight that monitoring disturbance levels during the reverse diffusion process can enhance gradient denoising capabilities, allowing Mjolnir to generate gradients that closely approximate the original, unperturbed versions through adaptive sampling steps. Extensive experiments demonstrate that Mjolnir effectively recovers the protected gradients and exposes the Federated Learning process to the threat of gradient leakage, achieving superior performance in gradient denoising and private data recovery.

cs.LG↗

arXiv2024

Empowering the Edge Intelligence by Air-Ground Integrated Federated Learning

Ubiquitous intelligence has been widely recognized as a critical vision of the future sixth generation (6G) networks, which implies the intelligence over the whole network from the core to the edge including end devices. Nevertheless, fulfilling such vision, particularly the intelligence at the edge, is extremely challenging, due to the limited resources of edge devices as well as the ubiquitous coverage envisioned by 6G. To empower the edge intelligence, in this article, we propose a novel framework called AGIFL (Air-Ground Integrated Federated Learning), which organically integrates air-ground integrated networks and federated learning (FL). In the AGIFL, leveraging the flexible on-demand 3D deployment of aerial nodes such as unmanned aerial vehicles (UAVs), all the nodes can collaboratively train an effective learning model by FL. We also conduct a case study to evaluate the effect of two different deployment schemes of the UAV over the learning and network performance. Last but not the least, we highlight several technical challenges and future research directions in the AGIFL.

cs.NI↗

arXiv2024

BrokerChain: A Blockchain Sharding Protocol by Exploiting Broker Accounts

State-of-the-art blockchain sharding solutions such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then produce hot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to address the hot-shard issue and how to reduce crossshard TXs become significant challenges of blockchain sharding. Through reviewing the related studies, we find that a crossshard TX protocol that can achieve workload balance among all shards and simultaneously reduce the quantity of crossshard TXs is still absent from the literature. To this end, we propose BrokerChain, which is a cross-shard blockchain protocol dedicated to account-based state sharding. Essentially, BrokerChain exploits fine-grained state partition and account segmentation. We also elaborate on how BrokerChain handles cross-shard TXs through broker accounts. The security issues and other properties of BrokerChain are analyzed rigorously. Finally, we conduct comprehensive evaluations using an opensource blockchain sharding prototype named BlockEmulator. The evaluation results show that BrokerChain outperforms other baselines in terms of transaction throughput, transaction confirmation latency, the queue size of the transaction pool, and workload balance.

cs.CR↗