arXiv subjects

Chao Feng

Publications and source records attributed to Chao Feng.

At least 91 records · Page 5Linked to original sources

arXiv2025

Demo: A Practical Testbed for Decentralized Federated Learning on Physical Edge Devices

Federated Learning (FL) enables collaborative model training without sharing raw data, preserving participant privacy. Decentralized FL (DFL) eliminates reliance on a central server, mitigating the single point of failure inherent in the traditional FL paradigm, while introducing deployment challenges on resource-constrained devices. To evaluate real-world applicability, this work designs and deploys a physical testbed using edge devices such as Raspberry Pi and Jetson Nano. The testbed is built upon a DFL training platform, NEBULA, and extends it with a power monitoring module to measure energy consumption during training. Experiments across multiple datasets show that model performance is influenced by the communication topology, with denser topologies leading to better outcomes in DFL settings.

cs.LG↗

arXiv2025

AugMixCloak: A Defense against Membership Inference Attacks via Image Transformation

Traditional machine learning (ML) raises serious privacy concerns, while federated learning (FL) mitigates the risk of data leakage by keeping data on local devices. However, the training process of FL can still leak sensitive information, which adversaries may exploit to infer private data. One of the most prominent threats is the membership inference attack (MIA), where the adversary aims to determine whether a particular data record was part of the training set. This paper addresses this problem through a two-stage defense called AugMixCloak. The core idea is to apply data augmentation and principal component analysis (PCA)-based information fusion to query images, which are detected by perceptual hashing (pHash) as either identical to or highly similar to images in the training set. Experimental results show that AugMixCloak successfully defends against both binary classifier-based MIA and metric-based MIA across five datasets and various decentralized FL (DFL) topologies. Compared with regularization-based defenses, AugMixCloak demonstrates stronger protection. Compared with confidence score masking, AugMixCloak exhibits better generalization.

cs.LG↗

arXiv2025

Intense X-ray Vortices Generation via Wavefront Shaping in high-gain Free-Electron Lasers

The x-ray vortex optical beam, distinguished by its topological charge and orbital angular momentum, offers new insights in probing complex electronic structures, enhancing material characterization, and advancing high-resolution imaging techniques. Here we propose a straightforward and effective method to generate intense x-ray vortices with tunable wavelengths in high-gain free-electron lasers (FELs). By simply adjusting the wavefront tilt of the radiation pulse during the FEL gain process, high-quality vortex beam can be amplified until saturation. Compared to existing methods for FEL vortex generation, the proposed technique imposes no wavelength limitations and can be easily implemented in high-gain FEL facilities, regardless of the operation modes.

physics.optics↗

arXiv2025

Enabling Continuous THz Band Coverage via Precise Electron Beam Tailoring in Free-electron Lasers

High-power, continuously tunable narrowband terahertz (THz) sources are essential for advancing nonlinear optics, THz-driven material dynamics, and ultrafast spectroscopy. Conventional techniques typically impose a trade-off between pulse energy and frequency tunability. Here, we introduce a novel free-electron laser approach that overcomes these limitations by pre-modulating a relativistic electron beam with a frequency-beating laser pulse and leveraging bunch compression along with collective effects to enhance microbunching. Experimental results demonstrate that this technique generates narrowband THz emission with continuous frequency tunability from 7.8 to 30.8THz, achieving pulse energies up to 385μJ while maintaining spectral bandwidths between 7.7% and 14.7%. Moreover, the method exhibits exceptional robustness and scalability, highlighting its unique ability to bridge the long-standing THz gap and offering a promising solution for diverse cutting-edge scientific applications.

physics.acc-ph↗

arXiv2025

A method for reversing the laser modulation in a Storage ring

The pursuit of coherent radiation generation remains a key direction in the advancement of storage ring light sources. Despite the potential of laser modulation in achieving this goal, it leads to a significant decline in the quality of the electron beam. Efforts to mitigate this decline have resulted in the proposal of demodulation schemes. However, implementing modulation and demodulation within the storage ring presents significant challenges due to dynamical and spatial constraints within straight sections. In this study, we propose a straightforward and easily implementable method for achieving reversible laser modulation in a storage ring. Notably, our approach circumvents the need for special storage ring requirements, such as lengthy straight sections or bypass section. Simulation results demonstrate a substantial restoration of beam quality following demodulation. This innovative scheme holds great promise for the realization of high repetition rate coherent storage ring light sources.

physics.acc-ph↗

arXiv2025

DMPA: Model Poisoning Attacks on Decentralized Federated Learning for Model Differences

Federated learning (FL) has garnered significant attention as a prominent privacy-preserving Machine Learning (ML) paradigm. Decentralized FL (DFL) eschews traditional FL's centralized server architecture, enhancing the system's robustness and scalability. However, these advantages of DFL also create new vulnerabilities for malicious participants to execute adversarial attacks, especially model poisoning attacks. In model poisoning attacks, malicious participants aim to diminish the performance of benign models by creating and disseminating the compromised model. Existing research on model poisoning attacks has predominantly concentrated on undermining global models within the Centralized FL (CFL) paradigm, while there needs to be more research in DFL. To fill the research gap, this paper proposes an innovative model poisoning attack called DMPA. This attack calculates the differential characteristics of multiple malicious client models and obtains the most effective poisoning strategy, thereby orchestrating a collusive attack by multiple participants. The effectiveness of this attack is validated across multiple datasets, with results indicating that the DMPA approach consistently surpasses existing state-of-the-art FL model poisoning attack strategies.

cs.LG↗

arXiv2025

De-VertiFL: A Solution for Decentralized Vertical Federated Learning

Federated Learning (FL), introduced in 2016, was designed to enhance data privacy in collaborative model training environments. Among the FL paradigm, horizontal FL, where clients share the same set of features but different data samples, has been extensively studied in both centralized and decentralized settings. In contrast, Vertical Federated Learning (VFL), which is crucial in real-world decentralized scenarios where clients possess different, yet sensitive, data about the same entity, remains underexplored. Thus, this work introduces De-VertiFL, a novel solution for training models in a decentralized VFL setting. De-VertiFL contributes by introducing a new network architecture distribution, an innovative knowledge exchange scheme, and a distributed federated training process. Specifically, De-VertiFL enables the sharing of hidden layer outputs among federation clients, allowing participants to benefit from intermediate computations, thereby improving learning efficiency. De-VertiFL has been evaluated using a variety of well-known datasets, including both image and tabular data, across binary and multiclass classification tasks. The results demonstrate that De-VertiFL generally surpasses state-of-the-art methods in F1-score performance, while maintaining a decentralized and privacy-preserving framework.

cs.LG↗

arXiv2025

Isolated attosecond free-electron laser based on a sub-cycle driver from hollow capillary fibers

The attosecond light source provides an advanced tool for investigating electron motion using time-resolved-spectroscopy techniques. Isolated attosecond pulses, especially, will significantly advance the study of electron dynamics. However, achieving high-intensity isolated attosecond pulses is still challenging at the present stage. In this paper, we propose a novel scheme for generating high-intensity, isolated attosecond soft X-ray free-electron lasers (FELs) using a mid-infrared (MIR) sub-cycle modulation laser from gas-filled hollow capillary fibers (HCFs). The multi-cycle MIR pulses are first compressed to sub-cycle using a helium-filled HCF with decreasing pressure gradient due to soliton self-compression effect. By utilizing such sub-cycle MIR laser pulse to modulate the electron beam, we can obtain a quasi-isolated current peak, which can then produce an isolated FEL pulse with high signal-to-noise ratio (SNR), naturally synchronizing with the sub-cycle MIR laser pulse. Numerical simulations have been carried out, including the sub-cycle pulse generation, electron beam modulation and FEL radiation processes. The simulation results indicate that an isolated attosecond pulse with wavelength of 1 nm, peak power of ~28 GW, pulse duration of ~600 attoseconds and SNR of ~96.4% can be generated by our proposed method. The numerical results demonstrated here pave a new way for generating the high-intensity isolated attosecond soft X-ray pulse, which may have many applications in nonlinear spectroscopy and atomic-site electronic process.

physics.acc-ph↗

arXiv2025

S-VOTE: Similarity-based Voting for Client Selection in Decentralized Federated Learning

Decentralized Federated Learning (DFL) enables collaborative, privacy-preserving model training without relying on a central server. This decentralized approach reduces bottlenecks and eliminates single points of failure, enhancing scalability and resilience. However, DFL also introduces challenges such as suboptimal models with non-IID data distributions, increased communication overhead, and resource usage. Thus, this work proposes S-VOTE, a voting-based client selection mechanism that optimizes resource usage and enhances model performance in federations with non-IID data conditions. S-VOTE considers an adaptive strategy for spontaneous local training that addresses participation imbalance, allowing underutilized clients to contribute without significantly increasing resource costs. Extensive experiments on benchmark datasets demonstrate the S-VOTE effectiveness. More in detail, it achieves lower communication costs by up to 21%, 4-6% faster convergence, and improves local performance by 9-17% compared to baseline methods in some configurations, all while achieving a 14-24% energy consumption reduction. These results highlight the potential of S-VOTE to address DFL challenges in heterogeneous environments.

cs.LG↗

arXiv2025

GPS as a Control Signal for Image Generation

We show that the GPS tags contained in photo metadata provide a useful control signal for image generation. We train GPS-to-image models and use them for tasks that require a fine-grained understanding of how images vary within a city. In particular, we train a diffusion model to generate images conditioned on both GPS and text. The learned model generates images that capture the distinctive appearance of different neighborhoods, parks, and landmarks. We also extract 3D models from 2D GPS-to-image models through score distillation sampling, using GPS conditioning to constrain the appearance of the reconstruction from each viewpoint. Our evaluations suggest that our GPS-conditioned models successfully learn to generate images that vary based on location, and that GPS conditioning improves estimated 3D structure.

cs.CV↗

arXiv2025

FedEP: Tailoring Attention to Heterogeneous Data Distribution with Entropy Pooling for Decentralized Federated Learning

Non-Independent and Identically Distributed (non-IID) data in Federated Learning (FL) causes client drift issues, leading to slower convergence and reduced model performance. While existing approaches mitigate this issue in Centralized FL (CFL) using a central server, Decentralized FL (DFL) remains underexplored. In DFL, the absence of a central entity results in nodes accessing a global view of the federation, further intensifying the challenges of non-IID data. Drawing on the entropy pooling algorithm employed in financial contexts to synthesize diverse investment opinions, this work proposes the Federated Entropy Pooling (FedEP) algorithm to mitigate the non-IID challenge in DFL. FedEP leverages Gaussian Mixture Models (GMM) to fit local data distributions, sharing statistical parameters among neighboring nodes to estimate the global distribution. Aggregation weights are determined using the entropy pooling approach between local and global distributions. By sharing only synthetic distribution information, FedEP preserves data privacy while minimizing communication overhead. Experimental results demonstrate that FedEP achieves faster convergence and outperforms state-of-the-art methods in various non-IID settings.

cs.LG↗

arXiv2024

The Performance of Seeded Free-Electron Lasers Through Dispersion Strength Tuning

Over the last decade, external seeded free electron lasers (FELs) have achieved significant advancements across various disciplines, progressively establishing themselves as indispensable tools in fields ranging from fundamental science to industrial applications. The performance of seeded FELs is critically dependent on the quality of the frequency up-conversion process. Optimized conditions for seeded FELs are typically considered as the maximization of the bunching factor. This paper discusses alternative perspectives on the optimization criteria for seeded FELs by analyzing the impact of dispersion strength on their overall performance. We investigate the relationship among the required dispersion strength for achieving the maximum bunching factor, maximum pulse energy, and optimal energy stability through theoretical analysis, simulation calculations, and experimental explorations. Additionally, the direct observation of pulse splitting emphasizes the consideration of trade-off between pulse energy and temporal coherence in seeded FELs. These results provide valuable insights and practical guidance for controlling the pulse characteristics of seeded FELs, contributing to the tuning and optimization of FEL facilities.

physics.acc-ph↗

arXiv2024

Leveraging MTD to Mitigate Poisoning Attacks in Decentralized FL with Non-IID Data

Decentralized Federated Learning (DFL), a paradigm for managing big data in a privacy-preserved manner, is still vulnerable to poisoning attacks where malicious clients tamper with data or models. Current defense methods often assume Independently and Identically Distributed (IID) data, which is unrealistic in real-world applications. In non-IID contexts, existing defensive strategies face challenges in distinguishing between models that have been compromised and those that have been trained on heterogeneous data distributions, leading to diminished efficacy. In response, this paper proposes a framework that employs the Moving Target Defense (MTD) approach to bolster the robustness of DFL models. By continuously modifying the attack surface of the DFL system, this framework aims to mitigate poisoning attacks effectively. The proposed MTD framework includes both proactive and reactive modes, utilizing a reputation system that combines metrics of model similarity and loss, alongside various defensive techniques. Comprehensive experimental evaluations indicate that the MTD-based mechanism significantly mitigates a range of poisoning attack types across multiple datasets with different topologies.

cs.CR↗

arXiv2024

CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation

Recent research has shown that the integration of Reinforcement Learning (RL) with Moving Target Defense (MTD) can enhance cybersecurity in Internet-of-Things (IoT) devices. Nevertheless, the practicality of existing work is hindered by data privacy concerns associated with centralized data processing in RL, and the unsatisfactory time needed to learn right MTD techniques that are effective against a rising number of heterogeneous zero-day attacks. Thus, this work presents CyberForce, a framework that combines Federated and Reinforcement Learning (FRL) to collaboratively and privately learn suitable MTD techniques for mitigating zero-day attacks. CyberForce integrates device fingerprinting and anomaly detection to reward or penalize MTD mechanisms chosen by an FRL-based agent. The framework has been deployed and evaluated in a scenario consisting of ten physical devices of a real IoT platform affected by heterogeneous malware samples. A pool of experiments has demonstrated that CyberForce learns the MTD technique mitigating each attack faster than existing RL-based centralized approaches. In addition, when various devices are exposed to different attacks, CyberForce benefits from knowledge transfer, leading to enhanced performance and reduced learning time in comparison to recent works. Finally, different aggregation algorithms used during the agent learning process provide CyberForce with notable robustness to malicious attacks.

cs.CR↗

arXiv2024

Sentinel: An Aggregation Function to Secure Decentralized Federated Learning

Decentralized Federated Learning (DFL) emerges as an innovative paradigm to train collaborative models, addressing the single point of failure limitation. However, the security and trustworthiness of FL and DFL are compromised by poisoning attacks, negatively impacting its performance. Existing defense mechanisms have been designed for centralized FL and they do not adequately exploit the particularities of DFL. Thus, this work introduces Sentinel, a defense strategy to counteract poisoning attacks in DFL. Sentinel leverages the accessibility of local data and defines a three-step aggregation protocol consisting of similarity filtering, bootstrap validation, and normalization to safeguard against malicious model updates. Sentinel has been evaluated with diverse datasets and data distributions. Besides, various poisoning attack types and threat levels have been verified. The results improve the state-of-the-art performance against both untargeted and targeted poisoning attacks when data follows an IID (Independent and Identically Distributed) configuration. Besides, under non-IID configuration, it is analyzed how performance degrades both for Sentinel and other state-of-the-art robust aggregation methods.

cs.DC↗

arXiv2024

Convolutional Beamspace Beamforming for Low-Complexity Far-Field and Near-Field MU-MIMO Communications

Inter-user interference (IUI) mitigation has been an essential issue for multi-user multiple-input multiple-output (MU-MIMO) communications. The commonly used linear processing schemes include the maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean squared error (MMSE) beamforming, which may result in the unfavorable performance or complexity as the antenna number grows. In this paper, we introduce a low-complexity linear beamforming solution for the IUI mitigation by using the convolutional beamspace (CBS) technique. Specifically, the dimension of channel matrix can be significantly reduced via the CBS preprocessing, thanks to its beamspace and spatial filtering effects. However, existing methods of the spatial filter design mainly benefit from the Vandermonde structure of channel matrix, which only holds for the far-field scenario with the uniform plane wave (UPW) model. As the antenna size increases, this characteristic may vanish in the near-field region of the array, where the uniform spherical wave (USW) propagation becomes dominant. To gain useful insights, we first investigate the beamforming design and performance analysis of the CBS-based beamforming based on the UPW model. Our results unveil that the proposed CBS-based MMSE beamforming is able to achieve a near-optimal performance but demands remarkably lower complexity than classical ZF and MMSE schemes. Furthermore, our analysis is also extended to the near-field case. To this end, a novel optimization-based CBS approach is proposed for preserving spatial filtering effects, thus rendering the compatibility of the CBS-based beamforming. Finally, numerical results are provided to demonstrate the effectiveness of our proposed CBS-based beamforming method.

eess.SP↗

arXiv2024

Enhancing Spatial Multiplexing and Interference Suppression for Near- and Far-Field Communications with Sparse MIMO

Multiple-input multiple-output has been a key technology for wireless systems for decades. For typical MIMO communication systems, antenna array elements are usually separated by half of the carrier wavelength, thus termed as conventional MIMO. In this paper, we investigate the performance of multi-user MIMO communication, with sparse arrays at both the transmitter and receiver side, i.e., the array elements are separated by more than half wavelength. Given the same number of array elements, the performance of sparse MIMO is compared with conventional MIMO. On one hand, sparse MIMO has a larger aperture, which can achieve narrower main lobe beams that make it easier to resolve densely located users. Besides, increased array aperture also enlarges the near-field communication region, which can enhance the spatial multiplexing gain, thanks to the spherical wavefront property in the near-field region. On the other hand, element spacing larger than half wavelength leads to undesired grating lobes, which, if left unattended, may cause severe inter-user interference. To gain further insights, we first study the spatial multiplexing gain of the basic single-user sparse MIMO communication system, where a closed-form expression of the near-field effective degree of freedom is derived. The result shows that the EDoF increases with the array sparsity for sparse MIMO before reaching its upper bound, which equals to the minimum value between the transmit and receive antenna numbers. Furthermore, the scaling law for the achievable data rate with varying array sparsity is analyzed and an array sparsity-selection strategy is proposed. We then consider the more general multi-user sparse MIMO communication system. It is shown that sparse MIMO is less likely to experience severe IUI than conventional MIMO.

eess.SP↗

arXiv2024

Integrated Sensing and Communication with Nested Array: Beam Pattern and Performance Analysis

Towards the upcoming 6G wireless networks, integrated sensing and communication (ISAC) has been identified as one of the typical usage scenarios. To further enhance the performance of ISAC, increasing the number of antennas as well as array aperture is one of the effective approaches. However, simply increasing the number of antennas will increase the cost of radio frequency chains and power consumption. To address this issue, in this paper, we consider an uplink ISAC system with nested array deployed at the base station. Nested array is a classic sparse array architecture that is able to enlarge the array aperture without increasing the number of physical antennas. While nested array for wireless sensing has been extensively studied, its potential for ISAC system has not been fully exploited. To fill this gap, in this paper, we provide the beam pattern analysis of nested arrays, and derive the closed-form expressions for the three beam pattern metrics, namely, the main lobe beam width, peak-to-local-minimum ratio, and prominent side lobes height. Extensive simulation results are provided to show that compared with conventional uniform arrays, nested arrays can achieve higher communication performance for densely located users while maintaining its advantage of sensing.

cs.IT↗