arXiv subjects

Md Arifuzzaman

Publications and source records attributed to Md Arifuzzaman.

10 recordsLinked to original sources

arXiv2026

HybridFLow: SDN-Orchestrated Client Partitioning for Hybrid Federated Learning

Cross-silo Federated Learning (FL) enables geographically distributed institutions to collaboratively train machine learning models without sharing raw data. In wide-area deployments, however, communication delays often dominate round completion time and exacerbate the straggler effect. Hybrid FL addresses this challenge by combining synchronous and asynchronous client participation, but effective partitioning requires visibility into network conditions such as shared bottlenecks, link utilization, and path contention that individual clients cannot observe. We present HybridFLow, a closed-loop SDN-driven orchestration framework that integrates network-layer intelligence directly into hybrid FL. Leveraging the SDN controller's global topology view, HybridFLow generates calibrated per-client communication-time estimates before each training round and uses them to partition clients into synchronous and asynchronous groups while balancing round latency and update staleness. After each round, measured communication times are fed back to the controller to continuously refine future predictions. Experimental results across multiple network topologies show that HybridFLow reaches 80% target accuracy 33-40% faster than SmartFLow and reduces average round duration by 30-40 seconds, while FedAsync fails to reach the target accuracy under non-IID data distributions.

cs.NI↗

arXiv2026

Scalable Quantum Key Distribution via GHZ Entanglement and Qubit Reuse

Conventional Quantum Key Distribution (QKD) requires the transmission of qubits proportional to or exceeding the length of the key, as protocols such as BB84 transmit more qubits than the final key size due to basis sifting and privacy amplification. Since quantum networks are still in their infancy and have limited capacity, this overhead puts significant pressure on network resources. To address this issue, we propose a Multi-Qubit Greenberger--Horne--Zeilinger (GHZ) State-based QKD scheme that reduces the number of qubits transmitted over the quantum channel. The proposed method transmits one GHZ qubit between endpoints and reuses the resulting entanglement to convey multiple classical key bits with the help of Quantum Non-Demolition (QND) measurements. Under the stated assumptions on authenticated classical communication, local reset verification, and bounded-error QND discrimination, one can transfer $L$ classical bits by generating an (L+1)-qubit GHZ state and transferring one qubit to the remote party. We verify correctness using the NetSquid quantum network simulator: the protocol achieves 100\% raw-key fidelity for keys of length up to 12 bits under both ideal conditions and depolarizing noise up to p = 0.005 per round. We further show that the proposed QKD algorithm can be extended to multi-party QKD and server-client deployment. The proposed scheme offers a transmitted-qubit-efficient, noise-tolerant alternative for bandwidth-limited quantum networks.

quant-ph↗

arXiv2026

Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing

Metal additive manufacturing (AM) enables the fabrication of safety-critical components, but reliable quality assurance depends on high-fidelity sensor streams containing proprietary process information, limiting collaborative data sharing. Existing defect-detection models typically treat melt-pool observations as independent samples, ignoring layer-wise physical couplings. Moreover, conventional privacy-preserving techniques, particularly Local Differential Privacy (LDP), lead to severe utility degradation because they inject uniform noise across all feature dimensions. To address these interrelated challenges, we propose FI-LDP-HGAT. This computational framework combines two methodological components: a stratified Hierarchical Graph Attention Network (HGAT) that captures spatial and thermal dependencies across scan tracks and deposited layers, and a feature-importance-aware anisotropic Gaussian mechanism (FI-LDP) for non-interactive feature privatization. Unlike isotropic LDP, FI-LDP redistributes the privacy budget across embedding coordinates using an encoder-derived importance prior, assigning lower noise to task-critical thermal signatures and higher noise to redundant dimensions while maintaining formal LDP guarantees. Experiments on a Directed Energy Deposition (DED) porosity dataset demonstrate that FI-LDP-HGAT achieves 81.5% utility recovery at a moderate privacy budget (epsilon = 4) and maintains defect recall of 0.762 under strict privacy (epsilon = 2), while outperforming classical ML, standard GNNs, and alternative privacy mechanisms, including DP-SGD across all evaluated metrics. Mechanistic analysis confirms a strong negative correlation (Spearman = -0.81) between feature importance and noise magnitude, providing interpretable evidence that the privacy-utility gains are driven by principled anisotropic allocation.

cs.LG↗

arXiv2025

Elastic Data Transfer Optimization with Hybrid Reinforcement Learning

Modern scientific data acquisition generates petabytes of data that must be transferred to geographically distant computing clusters. Conventional tools either rely on preconfigured sessions, which are difficult to tune for users without domain expertise, or they adaptively optimize only concurrency while ignoring other important parameters. We present \name, an adaptive data transfer method that jointly considers multiple parameters. Our solution incorporates heuristic-based parallelism, infinite pipelining, and a deep reinforcement learning based concurrency optimizer. To make agent training practical, we introduce a lightweight network simulator that reduces training time to less than four minutes and provides a $2750\times$ speedup compared to online training. Experimental evaluation shows that \name consistently outperforms existing methods across diverse datasets, achieving up to 9.5x higher throughput compared to state-of-the-art solutions.

cs.DC↗

arXiv2025

SmartFLow: A Communication-Efficient SDN Framework for Cross-Silo Federated Learning

Cross-silo Federated Learning (FL) enables multiple institutions to collaboratively train machine learning models while preserving data privacy. In such settings, clients repeatedly exchange model weights with a central server, making the overall training time highly sensitive to network performance. However, conventional routing methods often fail to prevent congestion, leading to increased communication latency and prolonged training. Software-Defined Networking (SDN), which provides centralized and programmable control over network resources, offers a promising way to address this limitation. To this end, we propose SmartFLow, an SDN-based framework designed to enhance communication efficiency in cross-silo FL. SmartFLow dynamically adjusts routing paths in response to changing network conditions, thereby reducing congestion and improving synchronization efficiency. Experimental results show that SmartFLow decreases parameter synchronization time by up to 47% compared to shortest-path routing and 41% compared to capacity-aware routing. Furthermore, it achieves these gains with minimal computational overhead and scales effectively to networks of up to 50 clients, demonstrating its practicality for real-world FL deployments.

cs.NI↗

arXiv2025

FLEET: A Federated Learning Emulation and Evaluation Testbed for Holistic Research

Federated Learning (FL) presents a robust paradigm for privacy-preserving, decentralized machine learning. However, a significant gap persists between the theoretical design of FL algorithms and their practical performance, largely because existing evaluation tools often fail to model realistic operational conditions. Many testbeds oversimplify the critical dynamics among algorithmic efficiency, client-level heterogeneity, and continuously evolving network infrastructure. To address this challenge, we introduce the Federated Learning Emulation and Evaluation Testbed (FLEET). This comprehensive platform provides a scalable and configurable environment by integrating a versatile, framework-agnostic learning component with a high-fidelity network emulator. FLEET supports diverse machine learning frameworks, customizable real-world network topologies, and dynamic background traffic generation. The testbed collects holistic metrics that correlate algorithmic outcomes with detailed network statistics. By unifying the entire experiment configuration, FLEET enables researchers to systematically investigate how network constraints, such as limited bandwidth, high latency, and packet loss, affect the convergence and efficiency of FL algorithms. This work provides the research community with a robust tool to bridge the gap between algorithmic theory and real-world network conditions, promoting the holistic and reproducible evaluation of federated learning systems.

cs.NI↗

arXiv2025

Adaptive Parallel Downloader for Large Genomic Datasets

Modern next-generation sequencing (NGS) projects routinely generate terabytes of data, which researchers commonly download from public repositories such as SRA or ENA. Existing download tools often employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times due to their inability to adapt to dynamic network conditions. We introduce FastBioDL, a parallel file downloader designed for large biological datasets, featuring an adaptive concurrency controller. FastBioDL frames the download process as an online optimization problem, utilizing a utility function and gradient descent to adjust the number of concurrent socket streams in real-time dynamically. This approach maximizes download throughput while minimizing resource overhead. Comprehensive evaluations on public genomic datasets demonstrate that FastBioDL achieves up to $4x$ speedup over state-of-the-art tools. Moreover, in high-speed network experiments, its adaptive design was up to $2.1x$ faster than existing tools. By intelligently optimizing standard HTTP or FTP downloads on the client side, FastBioDL provides a robust and efficient solution for large-scale genomic data acquisition, democratizing high-performance data retrieval for researchers without requiring specialized commercial software or protocols.

cs.DC↗

arXiv2025

Modular Architecture for High-Performance and Low Overhead Data Transfers

High-performance applications necessitate rapid and dependable transfer of massive datasets across geographically dispersed locations. Traditional file transfer tools often suffer from resource underutilization and instability because of fixed configurations or monolithic optimization methods. We propose AutoMDT, a novel modular data transfer architecture that employs a deep reinforcement learning based agent to simultaneously optimize concurrency levels for read, network, and write operations. Our solution incorporates a lightweight network-system simulator, enabling offline training of a Proximal Policy Optimization (PPO) agent in approximately 45 minutes on average, thereby overcoming the impracticality of lengthy online training in production networks. AutoMDT's modular design decouples I/O and network tasks, allowing the agent to capture complex buffer dynamics precisely and to adapt quickly to changing system and network conditions. Evaluations on production-grade testbeds show that AutoMDT achieves up to 8x faster convergence and a 68% reduction in transfer completion times compared with state-of-the-art solutions.

cs.DC↗

arXiv2025

Adaptive Entanglement Generation for Quantum Routing

Entanglement generation in long-distance quantum networks is a difficult process due to resource limitations and the probabilistic nature of entanglement swapping. To maximize success probability, existing quantum routing algorithms employ computationally expensive solutions (e.g., linear programming) to determine which links to entangle and use for end-to-end entanglement generation. Such optimization methods, however, cannot meet the delay requirements of real-world quantum networks, necessitating swift yet efficient real-time optimization models. In this paper, we propose reinforcement learning (RL)-based models to determine which links to entangle and proactively swap to meet connection requests. We show that the proposed RL-based approach is 20x faster compared to linear programming. Moreover, we show that one can take advantage of the longevity of entanglements to (i) cache entangled links for future use and (ii) proactively swap entanglement on high-demand path segments, thereby increasing the likelihood of request success. Through comprehensive simulations, we demonstrate that caching unused entanglements leads to a 10-15% improvement in the performance of state-of-the-art quantum routing algorithms. Complementing caching with proactive entanglement swapping further enhances the request success rate by up to 52.55%.

cs.NI↗

arXiv2022

Structural, elastic and optoelectronic properties of inorganic cubic FrBX3 (B = Ge, Sn; X = Cl, Br, I) perovskite: the density functional theory approach

Inorganic metal-halide cubic perovskite semiconductors have become more popular in industrial applications of photovoltaic and optoelectronic devices. Among various perovskites, lead-free materials are currently most explored due to their non-toxic effect on the environment. In this study, the structural, electronic, optical, and mechanical properties of lead-free cubic perovskite materials FrBX3 (B = Ge, Sn; X = Cl, Br, I) are investigated through first-principles density-functional theory (DFT) calculations. These materials are found to exhibit semiconducting behavior with direct bandgap energy and mechanical phase stability. The observed variation in the bandgap is explained based on the substitutions of cations and anions sitting over B and X-sites of the FrBX3 compounds. The high absorption coefficient, low reflectivity, and high optical conductivity make these materials suitable for photovoltaic and other optoelectronic device applications. It is observed that the material containing Ge (germanium) in the B-site has higher optical absorption and conductivity than Sn containing materials. A systematic analysis of the electronic, optical, and mechanical properties suggests that among all the perovskite materials, FrGeI3 would be a potential candidate for optoelectronic applications. The radioactive element Fr-containing perovskite FrGeI3 may have applications in nuclear medicine and diagnosis such as X-ray imaging technology.

cond-mat.mtrl-sci↗