arXiv subjects

Vini Chaudhary

Publications and source records attributed to Vini Chaudhary.

3 recordsLinked to original sources

arXiv2026

PERFECT: Personalized Federated Learning for CBRS Radar Detection

The Citizens Broadband Radio Service (CBRS) band is pivotal for expanding next-generation wireless services, but its success hinges on robustly protecting incumbent users, such as naval radar systems, from interference. This task is delegated to a network of Environmental Sensing Capability (ESC) sensors, which must detect faint radar signals amidst heavy co-channel interference from commercial LTE and 5G users. Traditional centralized detection models raise significant data privacy concerns and are ill-suited for the Non-Independent and Identically Distributed (non-IID) nature of data from geographically dispersed sensors. To overcome these limitations, we propose a novel Federated Learning (FL) framework PERFECT that leverages ESC level personalization for robust and efficient radar detection. PERFECT preserves privacy by training models locally on ESC sensors. Furthermore, our framework is the first to effectively handle non-IID scenarios through model personalization where different ESCs observe distinct radar types. We demonstrate through extensive simulations that PERFECT achieves the mandated 99% recall for radar detection, matching centralized performance while significantly enhancing privacy, efficiency, and scalability for dynamic spectrum sharing.

cs.NI↗

arXiv2026

O-DSS: An Open Dynamic Spectrum Sharing Framework for Cellular-Radar Coexistence in Mid-band Frequencies

The growing demand for mid-band spectrum necessitates efficient Dynamic Spectrum Sharing (DSS) to ensure coexistence between cellular networks and incumbent radar systems. Existing Spectrum Access System (SAS) frameworks rely on fixed Environmental Sensing Capability (ESC) sensors, which are latency-prone and inflexible. This paper introduces O-DSS, an O-RAN-compliant, Machine Learning (ML)-driven DSS framework that enables real-time cellular-radar coexistence in mid-band frequencies with shipborne and fast-moving airborne radars. O-DSS integrates radar detection from low-overhead Key Performance Metrics (KPMs) with spectrogram-based localization to drive fine-grained RAN control, including PRB blanking and radar-aware MCS adaptation. Deployed as a modular xApp, O-DSS achieves 60~ms detection and 700~ms evacuation latencies, outperforming existing baselines. Evaluations across simulations and Over-The-Air (OTA) testbeds show that O-DSS ensures robust incumbent protection while maintaining cellular performance by achieving radar detection of $\geq 99\%$ at SINR $\geq -4$~dB and localization recall of $\geq 95\%$ at SINR $\geq 8$~dB.

eess.SY↗

arXiv2025

Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges

The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-deployment. Machine Learning Operations (MLOps) has emerged as an integrative approach addressing these requirements by unifying relevant roles and streamlining ML workflows. As the MLOps market continues to grow, securing these pipelines has become increasingly critical. However, the unified nature of MLOps ecosystem introduces vulnerabilities, making them susceptible to adversarial attacks where a single misconfiguration can lead to compromised credentials, severe financial losses, damaged public trust, and the poisoning of training data. Our paper presents a systematic application of the MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) framework, supplemented by reviews of white and grey literature, to systematically assess attacks across different phases of the MLOps ecosystem. We begin by reviewing prior work in this domain, then present our taxonomy and introduce a threat model that captures attackers with different knowledge and capabilities. We then present a structured taxonomy of attack techniques explicitly mapped to corresponding phases of the MLOps ecosystem, supported by examples drawn from red-teaming exercises and real-world incidents. This is followed by a taxonomy of mitigation strategies aligned with these attack categories, offering actionable early-stage defenses to strengthen the security of MLOps ecosystem. Given the gradual evolution and adoption of MLOps, we further highlight key research gaps that require immediate attention. Our work emphasizes the importance of implementing robust security protocols from the outset, empowering practitioners to safeguard MLOps ecosystem against evolving cyber attacks.

cs.CR↗