arXiv subjects

Rajeev Sahay

Publications and source records attributed to Rajeev Sahay.

At least 19 recordsLinked to original sources

arXiv2026

Physics-Informed Direction-of-Arrival Estimation Over Distributed Edge Devices

Direction-of-arrival (DoA) estimation is a fundamental array processing task that has benefited substantially from deep learning. Deploying such methods across distributed edge devices introduces privacy and communication constraints that federated learning (FL) can address. Yet, standard FL algorithms treat DoA as a generic classification problem, ignoring the underlying physics of the array manifold. To address this, we propose a physics-informed FL framework for DoA estimation that incorporates steering-vector geometry directly into the local training objective via a manifold-aware regularizer. Unlike existing FL baselines, the regularizer in our framework penalizes discrepancies in steering space rather than label space, exploiting the known geometric structure of the array manifold. We provide theoretical convergence guarantees for our framework, showing convergence to a stationary point. Simulation results confirm that our physics informed approach outperforms multiple FL baseline approaches across iid and non-iid data conditions.

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arXiv2026

Spatiotemporal Link Formation Prediction in Social Learning Networks Using Graph Neural Networks

Social learning networks (SLNs) are graphical representations that capture student interactions within educational settings (e.g., a classroom), with nodes representing students and edges denoting interactions. Accurately predicting future interactions in these networks (i.e., link prediction) is crucial for enabling effective collaborative learning, supporting timely instructional interventions, and informing the design of effective group-based learning activities. However, traditional link prediction approaches are typically tuned to general online social networks (OSNs), often overlooking the complex, non-Euclidean, and dynamically evolving structure of SLNs, thus limiting their effectiveness in educational settings. In this work, we propose a graph neural network (GNN) framework that jointly considers the temporal evolution within classrooms and spatial aggregation across classrooms to perform link prediction in SLNs. Specifically, we analyze link prediction performance of GNNs over the SLNs of four distinct classrooms across their (i) temporal evolutions (varying time instances), (ii) spatial aggregations (joint SLN analysis), and (iii) varying spatial aggregations at varying temporal evolutions throughout the course. Our results indicate statistically significant performance improvements in the prediction of future links as the courses progress temporally. Aggregating SLNs from multiple classrooms generally enhances model performance as well, especially in sparser datasets. Moreover, we find that jointly leveraging both the temporal evolution and spatial aggregation of SLNs significantly outperforms conventional baseline approaches that analyze classrooms in isolation. Our findings demonstrate the efficacy of educationally meaningful link predictions, with direct implications for early-course decision-making and scalable learning analytics in and across classroom settings.

cs.SI↗

arXiv2025

REVERB-FL: Server-Side Adversarial and Reserve-Enhanced Federated Learning for Robust Audio Classification

Federated learning (FL) enables a privacy-preserving training paradigm for audio classification but is highly sensitive to client heterogeneity and poisoning attacks, where adversarially compromised clients can bias the global model and hinder the performance of audio classifiers. To mitigate the effects of model poisoning for audio signal classification, we present REVERB-FL, a lightweight, server-side defense that couples a small reserve set (approximately 5%) with pre- and post-aggregation retraining and adversarial training. After each local training round, the server refines the global model on the reserve set with either clean or additional adversarially perturbed data, thereby counteracting non-IID drift and mitigating potential model poisoning without adding substantial client-side cost or altering the aggregation process. We theoretically demonstrate the feasibility of our framework, showing faster convergence and a reduced steady-state error relative to baseline federated averaging. We validate our framework on two open-source audio classification datasets with varying IID and Dirichlet non-IID partitions and demonstrate that REVERB-FL mitigates global model poisoning under multiple designs of local data poisoning.

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arXiv2025

A Speculative GLRT-Backed Approach for Robust Deep Learning-Based Array Processing

Deep learning (DL) has recently emerged as an efficient approach for array processing tasks such as signal detection and direction of arrival. However, DL models lack statistical guarantees and, moreover, are highly susceptible to adversarial interference, raising security concerns about their reliability in adversarial wireless environments. In this letter, we first show that second-order statistics of the received array are spatially robust to L-p bounded adversarial perturbations. Then, motivated by this theoretical result, we develop an adversarially resilient speculative array processing framework that consists of a low-latency DL classifier backed by a theoretically-grounded generalized likelihood ratio test (GLRT) validator, which operates on the spatial domain of the array, where DL is used for fast speculative inference and later confirmed with the GLRT. Empirical evaluations under multiple L-p bounds, perturbation designs, and perturbation magnitudes corroborate our proposed framework and theoretical findings, demonstrating the superior performance of our proposed approach in comparison to multiple state-of-the-art baselines.

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arXiv2025

Bringing Multi-Modal Multi-Task Federated Foundation Models to Education Domain: Prospects and Challenges

Multi-modal multi-task (M3T) foundation models (FMs) have recently shown transformative potential in artificial intelligence, with emerging applications in education. However, their deployment in real-world educational settings is hindered by privacy regulations, data silos, and limited domain-specific data availability. We introduce M3T Federated Foundation Models (FedFMs) for education: a paradigm that integrates federated learning (FL) with M3T FMs to enable collaborative, privacy-preserving training across decentralized institutions while accommodating diverse modalities and tasks. Subsequently, this position paper aims to unveil M3T FedFMs as a promising yet underexplored approach to the education community, explore its potentials, and reveal its related future research directions. We outline how M3T FedFMs can advance three critical pillars of next-generation intelligent education systems: (i) privacy preservation, by keeping sensitive multi-modal student and institutional data local; (ii) personalization, through modular architectures enabling tailored models for students, instructors, and institutions; and (iii) equity and inclusivity, by facilitating participation from underrepresented and resource-constrained entities. We finally identify various open research challenges, including studying of (i) inter-institution heterogeneous privacy regulations, (ii) the non-uniformity of data modalities' characteristics, (iii) the unlearning approaches for M3T FedFMs, (iv) the continual learning frameworks for M3T FedFMs, and (v) M3T FedFM model interpretability, which must be collectively addressed for practical deployment.

cs.LG↗

arXiv2025

Privacy-Preserving Distributed Link Predictions Among Peers in Online Classrooms Using Federated Learning

Social interactions among classroom peers, represented as social learning networks (SLNs), play a crucial role in enhancing learning outcomes. While SLN analysis has recently garnered attention, most existing approaches rely on centralized training, where data is aggregated and processed on a local/cloud server with direct access to raw data. However, in real-world educational settings, such direct access across multiple classrooms is often restricted due to privacy concerns. Furthermore, training models on isolated classroom data prevents the identification of common interaction patterns that exist across multiple classrooms, thereby limiting model performance. To address these challenges, we propose one of the first frameworks that integrates Federated Learning (FL), a distributed and collaborative machine learning (ML) paradigm, with SLNs derived from students' interactions in multiple classrooms' online forums to predict future link formations (i.e., interactions) among students. By leveraging FL, our approach enables collaborative model training across multiple classrooms while preserving data privacy, as it eliminates the need for raw data centralization. Recognizing that each classroom may exhibit unique student interaction dynamics, we further employ model personalization techniques to adapt the FL model to individual classroom characteristics. Our results demonstrate the effectiveness of our approach in capturing both shared and classroom-specific representations of student interactions in SLNs. Additionally, we utilize explainable AI (XAI) techniques to interpret model predictions, identifying key factors that influence link formation across different classrooms. These insights unveil the drivers of social learning interactions within a privacy-preserving, collaborative, and distributed ML framework -- an aspect that has not been explored before.

cs.SI↗

arXiv2025

Robust Eavesdropping in the Presence of Adversarial Communications for RF Fingerprinting

Deep learning is an effective approach for performing radio frequency (RF) fingerprinting, which aims to identify the transmitter corresponding to received RF signals. However, beyond the intended receiver, malicious eavesdroppers can also intercept signals and attempt to fingerprint transmitters communicating over a wireless channel. Recent studies suggest that transmitters can counter such threats by embedding deep learning-based transferable adversarial attacks in their signals before transmission. In this work, we develop a time-frequency-based eavesdropper architecture that is capable of withstanding such transferable adversarial perturbations and thus able to perform effective RF fingerprinting. We theoretically demonstrate that adversarial perturbations injected by a transmitter are confined to specific time-frequency regions that are insignificant during inference, directly increasing fingerprinting accuracy on perturbed signals intercepted by the eavesdropper. Empirical evaluations on a real-world dataset validate our theoretical findings, showing that deep learning-based RF fingerprinting eavesdroppers can achieve classification performance comparable to the intended receiver, despite efforts made by the transmitter to deceive the eavesdropper. Our framework reveals that relying on transferable adversarial attacks may not be sufficient to prevent eavesdroppers from successfully fingerprinting transmissions in next-generation deep learning-based communications systems.

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arXiv2025

FLAME: A Federated Learning Approach for Multi-Modal RF Fingerprinting

Authorization systems are increasingly relying on processing radio frequency (RF) waveforms at receivers to fingerprint (i.e., determine the identity of) the corresponding transmitter. Federated learning (FL) has emerged as a popular paradigm to perform RF fingerprinting in networks with multiple access points (APs), as they allow effective deep learning-based device identification without requiring the centralization of locally collected RF signals stored at multiple APs. Yet, FL algorithms that operate merely on in-phase and quadrature (I/Q) time samples incur high convergence rates, resulting in excessive training rounds and inefficient training times. In this work, we propose FLAME: an FL approach for multi-modal RF fingerprinting. Our framework consists of simultaneously representing received RF waveforms in multiple complementary modalities beyond I/Q samples in an effort to reduce training times. We theoretically demonstrate the feasibility and efficiency of our methodology and derive a convergence bound that incurs lower loss and thus higher accuracies in the same training round in comparison to single-modal FL-based RF fingerprinting. Extensive empirical evaluations validate our theoretical results and demonstrate the superiority of FLAME in comparison to multiple considered baselines.

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arXiv2025

An Uncertainty Quantification Framework for Deep Learning-Based Automatic Modulation Classification

Deep learning has been shown to be highly effective for automatic modulation classification (AMC), which is a pivotal technology for next-generation cognitive communications. Yet, existing deep learning methods for AMC often lack robust mechanisms for uncertainty quantification (UQ). This limitation restricts their ability to produce accurate and reliable predictions in real-world environments, where signals can be perturbed as a result of several factors such as interference and low signal-to-noise ratios (SNR). To address this problem, we propose a deep ensemble approach that leverages multiple convolutional neural networks (CNNs) to generate predictive distributions, as opposed to point estimates produced by standard deep learning models, which produce statistical characteristics that quantify the uncertainty associated with each prediction. We validate our approach using real-world AMC data, evaluating performance through multiple UQ metrics in a variety of signal environments. Our results show that our proposed ensemble-based framework captures uncertainty to a greater degree compared to previously proposed baselines in multiple settings, including in-distribution samples, out-of-distribution samples, and low SNR signals. These findings highlight the strong UQ capabilities of our ensemble-based AMC approach, paving the way for more robust deep learning-based AMC.

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arXiv2025

The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directions

Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high school and college -- investigating AI/ML-driven mental health solutions within the education domain is of paramount importance. Nevertheless, conventional AI/ML techniques follow a centralized model training architecture, which poses privacy risks due to the need for transferring students' sensitive data from institutions, universities, and clinics to central servers. Federated learning (FL) has emerged as a solution to address these risks by enabling distributed model training while maintaining data privacy. Despite its potential, research on applying FL to analyze students' mental health remains limited. In this paper, we aim to address this limitation by proposing a roadmap for integrating FL into mental health data analysis within educational settings. We begin by providing an overview of mental health issues among students and reviewing existing studies where ML has been applied to address these challenges. Next, we examine broader applications of FL in the mental health domain to emphasize the lack of focus on educational contexts. Finally, we propose promising research directions focused on using FL to address mental health issues in the education sector, which entails discussing the synergies between the proposed directions with broader human-centered domains. By categorizing the proposed research directions into short- and long-term strategies and highlighting the unique challenges at each stage, we aim to encourage the development of privacy-conscious AI/ML-driven mental health solutions.

cs.CY↗

arXiv2023

Mitigating Evasion Attacks in Federated Learning-Based Signal Classifiers

Recent interest in leveraging federated learning (FL) for radio signal classification (SC) tasks has shown promise but FL-based SC remains susceptible to model poisoning adversarial attacks. These adversarial attacks mislead the ML model training process, damaging ML models across the network and leading to lower SC performance. In this work, we seek to mitigate model poisoning adversarial attacks on FL-based SC by proposing the Underlying Server Defense of Federated Learning (USD-FL). Unlike existing server-driven defenses, USD-FL does not rely on perfect network information, i.e., knowing the quantity of adversaries, the adversarial attack architecture, or the start time of the adversarial attacks. Our proposed USD-FL methodology consists of deriving logits for devices' ML models on a reserve dataset, comparing pair-wise logits via 1-Wasserstein distance and then determining a time-varying threshold for adversarial detection. As a result, USD-FL effectively mitigates model poisoning attacks introduced in the FL network. Specifically, when baseline server-driven defenses do have perfect network information, USD-FL outperforms them by (i) improving final ML classification accuracies by at least 6%, (ii) reducing false positive adversary detection rates by at least 10%, and (iii) decreasing the total number of misclassified signals by over 8%. Moreover, when baseline defenses do not have perfect network information, we show that USD-FL achieves accuracies of approximately 74.1% and 62.5% in i.i.d. and non-i.i.d. settings, outperforming existing server-driven baselines, which achieve 52.1% and 39.2% in i.i.d. and non-i.i.d. settings, respectively.

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arXiv2023

Defending Adversarial Attacks on Deep Learning Based Power Allocation in Massive MIMO Using Denoising Autoencoders

Recent work has advocated for the use of deep learning to perform power allocation in the downlink of massive MIMO (maMIMO) networks. Yet, such deep learning models are vulnerable to adversarial attacks. In the context of maMIMO power allocation, adversarial attacks refer to the injection of subtle perturbations into the deep learning model's input, during inference (i.e., the adversarial perturbation is injected into inputs during deployment after the model has been trained) that are specifically crafted to force the trained regression model to output an infeasible power allocation solution. In this work, we develop an autoencoder-based mitigation technique, which allows deep learning-based power allocation models to operate in the presence of adversaries without requiring retraining. Specifically, we develop a denoising autoencoder (DAE), which learns a mapping between potentially perturbed data and its corresponding unperturbed input. We test our defense across multiple attacks and in multiple threat models and demonstrate its ability to (i) mitigate the effects of adversarial attacks on power allocation networks using two common precoding schemes, (ii) outperform previously proposed benchmarks for mitigating regression-based adversarial attacks on maMIMO networks, (iii) retain accurate performance in the absence of an attack, and (iv) operate with low computational overhead.

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arXiv2023

How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?

There has been recent interest in leveraging federated learning (FL) for radio signal classification tasks. In FL, model parameters are periodically communicated from participating devices, training on their own local datasets, to a central server which aggregates them into a global model. While FL has privacy/security advantages due to raw data not leaving the devices, it is still susceptible to several adversarial attacks. In this work, we reveal the susceptibility of FL-based signal classifiers to model poisoning attacks, which compromise the training process despite not observing data transmissions. In this capacity, we develop an attack framework in which compromised FL devices perturb their local datasets using adversarial evasion attacks. As a result, the training process of the global model significantly degrades on in-distribution signals (i.e., signals received over channels with identical distributions at each edge device). We compare our work to previously proposed FL attacks and reveal that as few as one adversarial device operating with a low-powered perturbation under our attack framework can induce the potent model poisoning attack to the global classifier. Moreover, we find that more devices partaking in adversarial poisoning will proportionally degrade the classification performance.

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arXiv2023

Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach

We consider the problem of predicting link formation in Social Learning Networks (SLN), a type of social network that forms when people learn from one another through structured interactions. While link prediction has been studied for general types of social networks, the evolution of SLNs over their lifetimes coupled with their dependence on which topics are being discussed presents new challenges for this type of network. To address these challenges, we develop a series of autonomous link prediction methodologies that utilize spatial and time-evolving network architectures to pass network state between space and time periods, and that models over three types of SLN features updated in each period: neighborhood-based (e.g., resource allocation), path-based (e.g., shortest path), and post-based (e.g., topic similarity). Through evaluation on six real-world datasets from Massive Open Online Course (MOOC) discussion forums and from Purdue University, we find that our method obtains substantial improvements over Bayesian models, linear classifiers, and graph neural networks, with AUCs typically above 0.91 and reaching 0.99 depending on the dataset. Our feature importance analysis shows that while neighborhood and path-based features contribute the most to the results, post-based features add additional information that may not always be relevant for link prediction.

cs.SI↗

arXiv2022

A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions

Many communications and sensing applications hinge on the detection of a signal in a noisy, interference-heavy environment. Signal processing theory yields techniques such as the generalized likelihood ratio test (GLRT) to perform detection when the received samples correspond to a linear observation model. Numerous practical applications exist, however, where the received signal has passed through a nonlinearity, causing significant performance degradation of the GLRT. In this work, we propose prepending the GLRT detector with a neural network classifier capable of identifying the particular nonlinear time samples in a received signal. We show that pre-processing received nonlinear signals using our trained classifier to eliminate excessively nonlinear samples (i) improves the detection performance of the GLRT on nonlinear signals and (ii) retains the theoretical guarantees provided by the GLRT on linear observation models for accurate signal detection.

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arXiv2021

A Deep Ensemble-based Wireless Receiver Architecture for Mitigating Adversarial Attacks in Automatic Modulation Classification

Deep learning-based automatic modulation classification (AMC) models are susceptible to adversarial attacks. Such attacks inject specifically crafted wireless interference into transmitted signals to induce erroneous classification predictions. Furthermore, adversarial interference is transferable in black box environments, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification model. In this work, we propose a novel wireless receiver architecture to mitigate the effects of adversarial interference in various black box attack environments. We begin by evaluating the architecture uncertainty environment, where we show that adversarial attacks crafted to fool specific AMC DL architectures are not directly transferable to different DL architectures. Next, we consider the domain uncertainty environment, where we show that adversarial attacks crafted on time domain and frequency domain features to not directly transfer to the altering domain. Using these insights, we develop our Assorted Deep Ensemble (ADE) defense, which is an ensemble of deep learning architectures trained on time and frequency domain representations of received signals. Through evaluation on two wireless signal datasets under different sources of uncertainty, we demonstrate that our ADE obtains substantial improvements in AMC classification performance compared with baseline defenses across different adversarial attacks and potencies.

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arXiv2021

Mitigating Gradient-based Adversarial Attacks via Denoising and Compression

Gradient-based adversarial attacks on deep neural networks pose a serious threat, since they can be deployed by adding imperceptible perturbations to the test data of any network, and the risk they introduce cannot be assessed through the network's original training performance. Denoising and dimensionality reduction are two distinct methods that have been independently investigated to combat such attacks. While denoising offers the ability to tailor the defense to the specific nature of the attack, dimensionality reduction offers the advantage of potentially removing previously unseen perturbations, along with reducing the training time of the network being defended. We propose strategies to combine the advantages of these two defense mechanisms. First, we propose the cascaded defense, which involves denoising followed by dimensionality reduction. To reduce the training time of the defense for a small trade-off in performance, we propose the hidden layer defense, which involves feeding the output of the encoder of a denoising autoencoder into the network. Further, we discuss how adaptive attacks against these defenses could become significantly weak when an alternative defense is used, or when no defense is used. In this light, we propose a new metric to evaluate a defense which measures the sensitivity of the adaptive attack to modifications in the defense. Finally, we present a guideline for building an ordered repertoire of defenses, a.k.a. a defense infrastructure, that adjusts to limited computational resources in presence of uncertainty about the attack strategy.

cs.CR↗

arXiv2021

Frequency-based Automated Modulation Classification in the Presence of Adversaries

Automatic modulation classification (AMC) aims to improve the efficiency of crowded radio spectrums by automatically predicting the modulation constellation of wireless RF signals. Recent work has demonstrated the ability of deep learning to achieve robust AMC performance using raw in-phase and quadrature (IQ) time samples. Yet, deep learning models are highly susceptible to adversarial interference, which cause intelligent prediction models to misclassify received samples with high confidence. Furthermore, adversarial interference is often transferable, allowing an adversary to attack multiple deep learning models with a single perturbation crafted for a particular classification network. In this work, we present a novel receiver architecture consisting of deep learning models capable of withstanding transferable adversarial interference. Specifically, we show that adversarial attacks crafted to fool models trained on time-domain features are not easily transferable to models trained using frequency-domain features. In this capacity, we demonstrate classification performance improvements greater than 30% on recurrent neural networks (RNNs) and greater than 50% on convolutional neural networks (CNNs). We further demonstrate our frequency feature-based classification models to achieve accuracies greater than 99% in the absence of attacks.

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