arXiv subjects

Tamer ElBatt

Publications and source records attributed to Tamer ElBatt.

At least 19 recordsLinked to original sources

arXiv2025

ECGXtract: Deep Learning-based ECG Feature Extraction for Automated CVD Diagnosis

This paper presents ECGXtract, a deep learning-based approach for interpretable ECG feature extraction, addressing the limitations of traditional signal processing and black-box machine learning methods. In particular, we develop convolutional neural network models capable of extracting both temporal and morphological features with strong correlations to a clinically validated ground truth. Initially, each model is trained to extract a single feature, ensuring precise and interpretable outputs. A series of experiments is then carried out to evaluate the proposed method across multiple setups, including global versus lead-specific features, different sampling frequencies, and comparisons with other approaches such as ECGdeli. Our findings show that ECGXtract achieves robust performance across most features with a mean correlation score of 0.80 with the ground truth for global features, with lead II consistently providing the best results. For lead-specific features, ECGXtract achieves a mean correlation score of 0.822. Moreover, ECGXtract achieves superior results to the state-of-the-art open source ECGdeli as it got a higher correlation score with the ground truth in 90% of the features. Furthermore, we explore the feasibility of extracting multiple features simultaneously utilizing a single model. Semantic grouping is proved to be effective for global features, while large-scale grouping and lead-specific multi-output models show notable performance drops. These results highlight the potential of structured grouping strategies to balance the computational efficiency vs. model accuracy, paving the way for more scalable and clinically interpretable ECG feature extraction systems in limited resource settings.

eess.SP↗

arXiv2025

FedLLM-Align: Feature Extraction From Heterogeneous Clients

Federated learning (FL) enables collaborative model training without sharing raw data, making it attractive for privacy-sensitive domains, e.g., healthcare, finance, and IoT. A major obstacle, however, is the potential heterogeneity of tabular data across clients, in practical settings, where schema mismatches and incompatible feature spaces prevent straightforward aggregation. To address this challenge, this paper proposes FedLLM-Align, a federated learning framework that leverages pretrained transformer based language models for feature extraction. Towards this objective, FedLLM-Align serializes tabular records into text and derives semantically aligned embeddings from a pretrained LLM encoder, e.g, DistilBERT, facilitating lightweight local classifier heads that can be trained in a federated manner using standard aggregation schemes, e.g., FedAvg, while keeping all raw data records local. To quantify the merits and trade-offs of FedLLM-Align, we evaluate the proposed framework on binary classification tasks from two different domains: i) Coronary heart disease prediction on partitioned Framingham Heart Study data, and ii) Customer churn prediction on a financial dataset. FedLLM-Align outperforms state-of-the-art baselines by up to 25% in terms of the F1 score, under simulated schema heterogeneity, and achieves a 65% reduction in the communication overhead. These results establish FedLLM-Align as a privacy-preserving and communication-efficient approach for federated training based on clients with heterogeneous tabular datasets, commonly encountered in practice.

cs.LG↗

arXiv2025

FedCVD++: Communication-Efficient Federated Learning for Cardiovascular Risk Prediction with Parametric and Non-Parametric Model Optimization

Cardiovascular diseases (CVD) cause over 17 million deaths annually worldwide, highlighting the urgent need for privacy-preserving predictive systems. We introduce FedCVD++, an enhanced federated learning (FL) framework that integrates both parametric models (logistic regression, SVM, neural networks) and non-parametric models (Random Forest, XGBoost) for coronary heart disease risk prediction. To address key FL challenges, we propose: (1) tree-subset sampling that reduces Random Forest communication overhead by 70%, (2) XGBoost-based feature extraction enabling lightweight federated ensembles, and (3) federated SMOTE synchronization for resolving cross-institutional class imbalance. Evaluated on the Framingham dataset (4,238 records), FedCVD++ achieves state-of-the-art results: federated XGBoost (F1 = 0.80) surpasses its centralized counterpart (F1 = 0.78), and federated Random Forest (F1 = 0.81) matches non-federated performance. Additionally, our communication-efficient strategies reduce bandwidth consumption by 3.2X while preserving 95% accuracy. Compared to existing FL frameworks, FedCVD++ delivers up to 15% higher F1-scores and superior scalability for multi-institutional deployment. This work represents the first practical integration of non-parametric models into federated healthcare systems, providing a privacy-preserving solution validated under real-world clinical constraints.

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arXiv2024

MIRA: A Method of Federated MultI-Task Learning for LaRge LAnguage Models

In this paper, we introduce a method for fine-tuning Large Language Models (LLMs), inspired by Multi-Task learning in a federated manner. Our approach leverages the structure of each client's model and enables a learning scheme that considers other clients' tasks and data distribution. To mitigate the extensive computational and communication overhead often associated with LLMs, we utilize a parameter-efficient fine-tuning method, specifically Low-Rank Adaptation (LoRA), reducing the number of trainable parameters. Experimental results, with different datasets and models, demonstrate the proposed method's effectiveness compared to existing frameworks for federated fine-tuning of LLMs in terms of average and local performances. The proposed scheme outperforms existing baselines by achieving lower local loss for each client while maintaining comparable global performance.

cs.LG↗

arXiv2024

Fed-Sophia: A Communication-Efficient Second-Order Federated Learning Algorithm

Federated learning is a machine learning approach where multiple devices collaboratively learn with the help of a parameter server by sharing only their local updates. While gradient-based optimization techniques are widely adopted in this domain, the curvature information that second-order methods exhibit is crucial to guide and speed up the convergence. This paper introduces a scalable second-order method, allowing the adoption of curvature information in federated large models. Our method, coined Fed-Sophia, combines a weighted moving average of the gradient with a clipping operation to find the descent direction. In addition to that, a lightweight estimation of the Hessian's diagonal is used to incorporate the curvature information. Numerical evaluation shows the superiority, robustness, and scalability of the proposed Fed-Sophia scheme compared to first and second-order baselines.

cs.LG↗

arXiv2019

On Optimal Policies in Full-Duplex Wireless Powered Communication Networks

The optimal resource allocation scheme in a full-duplex Wireless Powered Communication Network (WPCN) composed of one Access Point (AP) and two wireless devices is analyzed and derived. AP operates in a full-duplex mode and is able to broadcast wireless energy signals in downlink and receive information data in uplink simultaneously. On the other hand, each wireless device is assumed to be equipped with Radio-Frequency (RF) energy harvesting circuitry which gathers the energy sent by AP and stores it in a finite capacity battery. The harvested energy is then used for performing uplink data transmission tasks. In the literature, the main focus so far has been on slot-oriented optimization. In this context, all the harvested RF energy in a given slot is also consumed in the same slot. However, this approach leads to sub-optimal solutions because it does not take into account the Channel State Information (CSI) variations over future slots. Differently from most of the prior works, in this paper we focus on the long-term weighted throughput maximization problem. This approach significantly increases the complexity of the optimization problem since it requires to consider both CSI variations over future slots and the evolution of the batteries when deciding the optimal resource allocation. We formulate the problem using the Markov Decision Process (MDP) theory and show how to solve it. Our numerical results emphasize the superiority of our proposed full-duplex WPCN compared to the half-duplex WPCN and reveal interesting insights about the effects of perfect as well as imperfect self-interference cancellation techniques on the network performance.

cs.IT↗

arXiv2018

A Converse Bound for Cache-Aided Interference Networks

In this paper, an interference network with arbitrary number of transmitters and receivers is studied, where each transmitter is equipped with a finite size cache. We obtain an information-theoretic lower bound on both the peak normalized delivery time (NDT), and the \textit{expected} NDT of cache-aided interference networks with uniform content popularity. For the peak NDT, we show that our lower bound is strictly tighter than the bound in the literature for small cache sizes. Moreover, we show that the feasibility region on the expected NDT is bigger than that of the peak NDT.

cs.IT↗

arXiv2018

Towards Optimal Resource Allocation in Wireless Powered Communication Networks with Non-Orthogonal Multiple Access

The optimal allocation of time and energy resources is characterized in a Wireless Powered Communication Network (WPCN) with non-Orthogonal Multiple Access (NOMA). We consider two different formulations; in the first one (max-sum), the sum-throughput of all users is maximized. In the second one (max-min), and targeting fairness among users, we consider maximizing the min-throughput of all users. Under the above two formulations, two NOMA decoding schemes are studied, namely, low complexity decoding (LCD) and successive interference cancellation decoding (SICD). Due to the non-convexity of three of the studied optimization problems, we consider an approximation approach, in which the non-convex optimization problem is approximated by a convex optimization problem, which satisfies all the constraints of the original problem. The approximated convex optimization problem can then be solved iteratively. The results show a trade-off between maximizing the sum throughout and achieving fairness through maximizing the minimum throughput.

math.OC↗

arXiv2018

Stable Throughput of Cooperative Cognitive Networks with Energy Harvesting: Finite Relay Buffer and Finite Battery Capacity

This paper studies a generic model for cooperative cognitive radio networks where the secondary user is equipped with a finite relay queue as well as a finite battery queue. Our prime objective is to characterize the stable throughput region. Nevertheless, the complete characterization of the stable throughput region for such system is notoriously difficult, since the computation of the steady state distribution of the two-dimensional Markov Chain (MC) model for both finite queues is prohibitively complex. We first propose an algorithm to characterize the stable throughput region numerically, and show its sheer computational complexity for large queue lengths. To lend tractability and explore the nature of design parameters optimization at the cognitive node, we next focus on two simpler systems, namely, finite battery queue with infinite relay queue and finite relay queue with infinite battery queue (referred henceforth as dominant system $1$ and $2$, respectively). For each proposed dominant system, we investigate the maximum service rate of the cognitive node subject to stability conditions. Despite the complexity of the formulated optimization problems, due to their non-convexity, we exploit the problems' structure to transform them into linear programs. Thus, we are able to solve them efficiently using standard linear programming solvers. Our numerical results demonstrate that, in practical systems, finite battery and relay queues achieve the same level of benefits of a system with infinite queue sizes, when their sizes are sufficiently large. They also reveal that the achievable stable throughput region significantly expands when the arrival rate of the energy harvesting process increases.

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arXiv2017

Degrees of Freedom of Interference Networks with Transmitter-Side Caches

This paper studies cache-aided interference networks with arbitrary number of transmitters and receivers, whereby each transmitter has a cache memory of finite size. Each transmitter fills its cache memory from a content library of files in the placement phase. In the subsequent delivery phase, each receiver requests one of the library files, and the transmitters are responsible for delivering the requested files from their caches to the receivers. The objective is to design schemes for the placement and delivery phases to maximize the sum degrees of freedom (sum-DoF) which expresses the capacity of the interference network at the high signal-to-noise ratio regime. Our work mainly focuses on a commonly used uncoded placement strategy. We provide an information-theoretic bound on the sum-DoF for this placement strategy. We demonstrate by an example that the derived bound is tighter than the bounds existing in the literature for small cache sizes. We propose a novel delivery scheme with a higher achievable sum-DoF than those previously given in the literature. The results reveal that the reciprocal of sum-DoF decreases linearly as the transmitter cache size increases. Therefore, increasing cache sizes at transmitters translates to increasing the sum-DoF and, hence, the capacity of the interference networks.

cs.IT↗

arXiv2017

Optimization of Energy-Constrained Wireless Powered Communication Networks with Heterogeneous Nodes

In this paper, we study wireless networks where nodes have two energy sources, namely a battery and radio frequency (RF) energy harvesting circuitry. We formulate two optimization problems with different objective functions, namely maximizing the sum throughput and maximizing the minimum throughput, for enhanced fairness. Furthermore, we show the generality of the proposed system model through characterizing the conditions under which the two formulated optimization problems can be reduced to the corresponding problems of different known wireless networks, namely, conventional wireless networks (battery-powered) and wireless powered communications networks (WPCNs) with only RF energy harvesting nodes. In addition, we introduce WPCNs with two types of nodes, with and without RF energy harvesting capability, in which the nodes without RF energy harvesting are utilized to enhance the sum throughput, even beyond WPCNs with all energy harvesting nodes. We establish the convexity of all formulated problems which opens room for efficient solution using standard techniques. Our numerical results show that the two types of wireless networks, namely WPCNs with only RF energy harvesting nodes and conventional wireless networks, are considered, respectively, as lower and upper bounds on the performance of the generalized problem setting in terms of the maximum sum throughput and the maxmin throughput. Moreover, the results reveal new insights and throughput-fairness trade-offs unique to our new problem setting.

cs.IT↗

arXiv2017

Non-Orthogonal Multiple Access Schemes in Wireless Powered Communication Networks

We characterize time and power allocations to optimize the sum-throughput of a Wireless Powered Communication Network (WPCN) with Non-Orthogonal Multiple Access (NOMA). In our setup, an Energy Rich (ER) source broadcasts wireless energy to several devices, which use it to simultaneously transmit data to an Access Point (AP) on the uplink. Differently from most prior works, in this paper we consider a generic scenario, in which the ER and AP do not coincide, i.e., are two separate entities. We study two NOMA decoding schemes, namely Low Complexity Decoding (LCD) and Successive Interference Cancellation Decoding (SICD). For each scheme, we formulate a sum-throughput optimization problem over a finite horizon. Despite the complexity of the LCD optimization problem, due to its non-convexity, we recast it into a series of geometric programs. On the other hand, we establish the convexity of the SICD optimization problem and propose an algorithm to find its optimal solution. Our numerical results demonstrate the importance of using successive interference cancellation in WPCNs with NOMA, and show how the energy should be distributed as a function of the system parameters.

cs.IT↗

arXiv2017

Decentralized Coded Caching in Wireless Networks: Trade-off between Storage and Latency

This paper studies the decentralized coded caching for a Fog Radio Access Network (F-RAN), whereby two edge-nodes (ENs) connected to a cloud server via fronthaul links with limited capacity are serving the requests of $K_r$ users. We consider all ENs and users are equipped with caches. A decentralized content placement is proposed to independently store contents at each network node during the off-peak hours. After that, we design a coded delivery scheme in order to deliver the user demands during the peak-hours under the objective of minimizing the normalized delivery time (NDT), which refers to the worst case delivery latency. An information-theoretic lower bound on the minimum NDT is derived for arbitrary number of ENs and users. We evaluate numerically the performance of the decentralized scheme. Additionally, we prove the approximate optimality of the decentralized scheme for a special case when the caches are only available at the ENs.

cs.IT↗

arXiv2017

Cache-Aided Heterogeneous Networks: Coverage and Delay Analysis

This paper characterizes the performance of a generic $K$-tier cache-aided heterogeneous network (CHN), in which the base stations (BSs) across tiers differ in terms of their spatial densities, transmission powers, pathloss exponents, activity probabilities conditioned on the serving link and placement caching strategies. We consider that each user connects to the BS which maximizes its average received power and at the same time caches its file of interest. Modeling the locations of the BSs across different tiers as independent homogeneous Poisson Point processes (HPPPs), we derive closed-form expressions for the coverage probability and local delay experienced by a typical user in receiving each requested file. We show that our results for coverage probability and delay are consistent with those previously obtained in the literature for a single tier system.

cs.IT↗

arXiv2016

Asymmetric Degrees of Freedom of the Full-Duplex MIMO 3-Way Channel with Unicast and Broadcast Messages

In this paper, we characterize the asymmetric total degrees of freedom (DoF) of a multiple-input multiple-output (MIMO) 3-way channel. Each node has a separate-antenna full-duplex MIMO transceiver with a different number of antennas, where each antenna can be configured for either signal transmission or reception. We study this system under two message configurations; the first configuration is when each node has two unicast messages to be delivered to the two other nodes, while the second configuration is when each node has two unicast messages as well as one broadcast message to be delivered to the two other nodes. For each configuration, we first derive upper bounds on the total DoF of the system. Cut-set bounds in conjunction with genie-aided bounds are derived to characterize the achievable total DoF. Afterwards, we analytically derive the optimal number of transmit and receive antennas at each node to maximize the total DoF of the system, subject to the total number of antennas at each node. Finally, the achievable schemes for each configuration are constructed. The proposed schemes are mainly based on zero-forcing and null-space transmit beamforming.

cs.IT↗

arXiv2016

On Optimizing Cooperative Cognitive User Performance under Primary QoS Constraints

We study the problem of optimizing the performance of cognitive radio users with opportunistic real-time applications subject to primary users quality-of-service (QoS) constraints. Two constrained optimization problems are formulated; the first problem is maximizing the secondary user throughput while the second problem is minimizing the secondary user average delay, subject to a common constraint on the primary user average delay. In spite of the complexity of the optimization problems, due to their non-convexity, we transform the first problem into a set of linear programs and the second problem into a set of quasiconvex optimization problems. We prove that both problems are equivalent with identical feasible sets and optimal solutions. We show, through numerical results, that the proposed cooperation policy represents the best compromise between enhancing the secondary users QoS and satisfying the primary users QoS requirements.

cs.NI↗

arXiv2016

Energy-Aware Cooperative Wireless Networks with Multiple Cognitive Users

In this paper, we study and analyze cooperative cognitive radio networks with arbitrary number of secondary users (SUs). Each SU is considered a prospective relay for the primary user (PU) besides having its own data transmission demand. We consider a multi-packet transmission framework which allows multiple SUs to transmit simultaneously thanks to dirty-paper coding. We propose power allocation and scheduling policies that optimize the throughput for both PU and SU with minimum energy expenditure. The performance of the system is evaluated in terms of throughput and delay under different opportunistic relay selection policies. Towards this objective, we present a mathematical framework for deriving stability conditions for all queues in the system. Consequently, the throughput of both primary and secondary links is quantified. Furthermore, a moment generating function (MGF) approach is employed to derive a closed-form expression for the average delay encountered by the PU packets. Results reveal that we achieve better performance in terms of throughput and delay at lower energy cost as compared to equal power allocation schemes proposed earlier in literature. Extensive simulations are conducted to validate our theoretical findings.

cs.NI↗

arXiv2015

Stability Analysis of Slotted Aloha with Opportunistic RF Energy Harvesting

Energy harvesting (EH) is a promising technology for realizing energy efficient wireless networks. In this paper, we utilize the ambient RF energy, particularly interference from neighboring transmissions, to replenish the batteries of the EH enabled nodes. However, RF energy harvesting imposes new challenges into the analysis of wireless networks. Our objective in this work is to investigate the performance of a slotted Aloha random access wireless network consisting of two types of nodes, namely Type I which has unlimited energy supply and Type II which is solely powered by an RF energy harvesting circuit. The transmissions of a Type I node are recycled by a Type II node to replenish its battery. We characterize an inner bound on the stable throughput region under half-duplex and full-duplex energy harvesting paradigms as well as for the finite capacity battery case. We present numerical results that validate our analytical results, and demonstrate their utility for the analysis of the exact system.

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