Pith. sign in

REVIEW 4 major objections 7 minor 37 references

Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise

T0 review · 4 major / 7 minor · reviewed 2026-08-06 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Scaling updates by noise intensity boosts quantum federated learning

desk verdict SpoQFL is a plausible heuristic for noise-aware QFL, but the central algorithm needs access to an unobservable quantity and the evidence is single-run simulation; worth a referee but not acceptance as-is. read the letter →

arxiv 2507.12492 v1 pith:GJXCGCMQ submitted 2025-07-15 quant-ph cs.AIcs.LG

Ratun Rahman , Atit Pokharel , Dinh C. Nguyen This is my paper · ORCID
classification quant-phcs.AIcs.LG
keywords sporadicfederatedlearningquantumnoisemitigationheterogeneityneuralnetworksNISQconvergencestabilityCIFARclassification
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

SpoQFL is a federated learning method for quantum devices that targets a specific obstacle: quantum noise levels differ across devices, and that heterogeneity destabilizes the shared model. The paper's central claim is that if each client shrinks its gradient update by a factor that depends on the local noise deviation, and skips the update when that deviation is large, the global model becomes more accurate and converges more smoothly. Experiments on CIFAR-10 and CIFAR-100 show accuracy gains over the strongest prior QFL baseline by up to 4.87% and 3.66%, and a reduction in loss by 16.84% and 4.15%. If correct, this gives QFL a simple client-side rule for robustness to heterogeneous hardware noise that requires no change to the server-side aggregation.

What carries the argument

The central object is the sporadic variable $x_{n,k}^t = \exp(-\gamma |\xi_{n,k}^t|)$, defined in Eq. (9) from the norm of the noise-induced gradient deviation. Each client multiplies its gradient estimate by this factor before the local update (Eq. (10)), and the update is skipped entirely when the factor falls below the threshold $\tau$ (Algorithm 1, lines 10-11). The paper ties this mechanism to the convergence bound in Eq. (8) by arguing that the scaling shrinks the noise variance term $V$, which accelerates convergence and stabilizes training across clients with heterogeneous noise.

What would settle it

A benchmark that removes the oracle access to the true gradient—for example, running SpoQFL on a real NISQ device or in a simulator where clients must estimate $|\xi|$ from shot statistics—and shows the accuracy advantage over QFL vanishes or reverses would falsify the paper's central claim.

Watch

Extended reading notes

Core claim

The paper claims that SpoQFL, a sporadic learning strategy, significantly outperforms conventional QFL approaches under simulated Pauli noise, achieving superior training performance and more stable convergence. On the reported benchmarks, SpoQFL reaches 91.92% accuracy on CIFAR-10 and 57.60% on CIFAR-100, up to 4.87% and 3.66% above the best prior quantum federated method (wpQFL). The mechanism is the sporadic variable $x = \exp(-\gamma |\xi|)$, which scales the noisy gradient so that noisy clients contribute less to the global model, and a threshold $\tau$ that skips the most unstable updates. The paper also argues this reduces the noise variance term in the existing convergence bound, improving both stability and final performance.

Load-bearing premise

The core mechanism requires each client to know the size of the noise-induced error in its own gradient, but a real client only has access to the noisy gradient itself, so the scaling and skip rules cannot be computed on actual hardware without some estimate for the noise-free gradient.

Editorial extensions

If this is right

  • SpoQFL needs no change to the server aggregation equation, so it can be dropped into existing QFL frameworks that use FedAvg-style averaging.
  • The reported accuracy gains (up to 4.87% on CIFAR-10 and 3.66% on CIFAR-100) suggest that sporadic attenuation is a competitive alternative to personalized QFL baselines like wpQFL.
  • Because the method reduces the noise variance term in the convergence bound, it should also help in other noisy-gradient settings, such as classical FL with heterogeneous communication noise.
  • The scaling parameter $\gamma$ and threshold $\tau$ give practitioners two tunable knobs for trading off update informativeness against noise suppression.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The method as presented assumes clients can measure the noise-induced deviation $|\xi|$; a practical implementation would need a proxy for this quantity, such as the variance of measurement shots or a separate calibration round, and testing with such a proxy is a direct extension the paper does not report.
  • The main comparison is run at a single noise level (epsilon = 0.001); a natural extension is to sweep the noise level and check whether the relative advantage of SpoQFL grows or shrinks as noise increases.
  • The same scaling idea could be applied server-side by weighting clients according to estimated noise, which would relax the client-side assumption that the true gradient deviation is known.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 7 minor

Summary. The paper proposes SpoQFL, a sporadic federated learning scheme for quantum neural network classifiers. In each local step, the noisy gradient estimate is decomposed as the noise-free gradient plus a noise-induced deviation xi, and the parameter update is scaled by exp(-gamma*|xi|) and skipped entirely when this factor falls below a threshold tau. The authors motivate the method by an appeal to a standard stochastic-convergence bound and evaluate it in torchquantum simulations on CIFAR-10 and CIFAR-100 with ten clients and heterogeneous encodings, reporting accuracy gains of up to 4.87% over wpQFL on CIFAR-10 and 3.66% on CIFAR-100, with corresponding loss reductions. The paper also contains ablations over qubit counts, layer counts, loss functions, learning rates, client counts, and noise levels.

Significance. The high-level idea of suppressing locally noisy quantum updates before federated aggregation is reasonable and could be valuable for heterogeneous NISQ devices. The paper is clearly organized, provides pseudocode for the algorithm, and includes broad ablations and comparisons against several classical and quantum baselines. However, the central mechanism depends on the noise-induced gradient deviation xi, which a client cannot observe on real hardware and for which no estimator is supplied; the convergence justification in Section 3.4 is asserted rather than proved; and the experimental section lacks error bars, seed counts, and key hyperparameter values. If the mechanism were implementable and the gains reproducible, this would be a useful contribution, but as written the central claim is not established.

major comments (4)
  1. [Algorithm 1, Eq. (9)] The sporadic variable in Eq. (9) and the skip rule in Algorithm 1 (lines 9-11) are functions of xi_t^{n,k}, the difference between the noisy gradient estimate and the noise-free gradient defined in Eq. (6). A client on a real device observes only the noisy gradient estimate; the noise-free gradient is the quantity being learned. The manuscript provides no estimator for |xi|, no repeated-measurement variance estimator, no zero-noise extrapolation procedure, and no sensitivity analysis to mis-specified xi. In the torchquantum simulations, the authors can compute the true noiseless gradient and subtract it, so Table 7 appears to rely on simulator-only knowledge. Because this inaccessible quantity drives both the scaling and the skipping mechanism, the algorithmic proposal is not implementable as stated.
  2. [Section 3.4, Eqs. (8)-(10)] The claim that sporadic learning directly reduces the noise variance term V in Eq. (8) is not proved. Multiplying the noisy gradient by x_t^{n,k} = exp(-gamma*|xi|) changes the expectation of the update as well as its variance, so a bias-variance decomposition is required to show that the V term decreases and that the induced bias is controlled. Eq. (8) is a standard bound for unmodified stochastic gradients, and no argument is given that the modified update satisfies the same contraction with a smaller V. The notation in Eq. (8) also conflates the noise deviation xi with the loss suboptimality E[L(omega^T)] - L*, which further obscures the argument.
  3. [Section 4.3, Table 2 and Section 4.5, Table 7] The layer-count ablation in Table 2 shows that l=3 is best for CIFAR-10 (89.36%) and l=1 is best for CIFAR-100 (55.63%), but the text states that l=1 is selected for subsequent evaluations without qualification. Since Table 7 reports a single configuration for both datasets, the choice contradicts the reported ablation and leaves the experimental setup ambiguous. In addition, the headline comparison in Table 7 is presented without error bars, without the number of random seeds, and without the values of gamma and tau; hyperparameters are selected on test accuracy, which risks overfitting the evaluation protocol.
  4. [Sections 4.4-4.5, Tables 6-7] The noise level used for the Table 7 comparison is not stated. Figure 3 uses epsilon=0.001, while Table 6 spans epsilon from 0.001 to 0.5, and the main comparison table gives no indication of which noise regime is used or whether all baselines are evaluated under identical noise conditions. Without this information, the reported improvements over wpQFL and other baselines cannot be reproduced or interpreted.
minor comments (7)
  1. [Eq. (7)] There is a typo 'wshere' instead of 'where', and the symbols N_h, D, H, and M are introduced without a precise definition of the Hermitian observable decomposition.
  2. [Figure 2] Panel (d) is captioned 'CIFAR-10 Non-IID accuracy', but it is placed after the CIFAR-100 IID panel and the text describes it as part of the CIFAR-100 comparison; the caption should be corrected.
  3. [Section 4.3, Table 2] The text says layer configurations 1, 2, 3, 5, and 10 were tested, but the table also contains a row for l=4; the list and table should be reconciled.
  4. [Section 4.3] The notation switches between Dq and Dc for the number of qubits; these should be unified.
  5. [Figure 3] The axis labels of the convergence plots are garbled in the typeset version, with values such as '0 25 50 75 10 0' appearing on the vertical axis; the figures need to be regenerated.
  6. [Abstract and Section 4.1] The abstract refers to 'real-world datasets', but the experiments are simulations using torchquantum with synthetic noise; the wording should be adjusted to avoid overclaiming.
  7. [Related work, Section 2.2] The connection to the cited sporadic federated learning work [37] is mentioned only briefly; a short comparison of what is new in the quantum setting relative to [37] would help the reader.

Circularity Check

1 steps flagged · score 4.0 of 10

The sporadic variable is defined as a decreasing function of the noise deviation, so the qualitative suppression direction is built into Eq. (9); the convergence claim is asserted rather than derived, and the algorithm requires the unobservable noiseless gradient in simulation.

  1. self definitional [Section 3.4, Eqs. (9)-(10); Algorithm 1, lines 8-13]
    "Given the noise deviation ξt n,k in a client's gradient estimate, the sporadic variable is defined as xt n,k = exp(−γ|ξt n,k|) ... For clients experiencing high noise, xt n,k becomes small, limiting the impact of unstable updates. ... Since sporadic learning directly reduces the noise variance term V in the previously established error bound in (8), it improves both convergence stability and model performance."

    The central noise-suppression claim is a restatement of Eq. (9): x is constructed to be small precisely when |ξ| is large, so 'limiting the impact of unstable updates' is the definition of x, not a derived consequence. The follow-up assertion that this reduces V in Eq. (8) merely restates that the update in Eq. (10) is scaled by x^2 ≤ 1; no proof is given that the bias-variance trade-off actually improves convergence, and no estimator for ξ is supplied. In the simulations, ξ is computed from Eq. (6) using the simulator's noiseless gradient ∇f, so the algorithm is fed the very quantity a real client cannot observe; the qualitative result is therefore equivalent to the construction, while the quantitative Table 7 gains may depend on oracle knowledge of the true gradient.

full rationale

No load-bearing self-citation chain exists: the paper cites prior work on sporadic FL ([37]) and its own prior work on PFL ([26]) only as baselines, not to justify the central mechanism. The main circularity concern is that the sporadic variable in Eq. (9) is defined as a decreasing function of |ξ|, the noise-induced gradient deviation; therefore the statement that high-noise clients are suppressed is tautological. The paper then asserts, without proof, that this reduces the noise variance term V in the convergence bound (8) and thereby improves convergence stability; that assertion is neither derived nor separated from the definition, and it neglects the bias induced by scaling updates by a function of ξ. A further non-circular but serious problem is that ξ = ĝ − ∇f requires knowing the noiseless gradient, which is unavailable on a real client; Algorithm 1 is thus implemented in simulation using the ground-truth noiseless gradient from the simulator. The accuracy improvements in Table 7 are measured, not derived, so the central empirical claim has independent experimental content, but it is compromised by the oracle input. Overall, the qualitative mitigation direction is definitional and the convergence justification is unsupported, while the headline performance comparison is not itself a circular reduction; hence a moderate score of 4 is appropriate.

Assumptions & free parameters 5 free parameters · 5 assumptions · 0 invented entities

The ledger shows the central method rests on two ad hoc assumptions: knowing the unobservable noise deviation, and the unproved claim that scaling reduces the variance term in the convergence bound. The free parameters gamma, tau, learning rate, layer count, and qubit count are chosen using test-set performance, so the reported gains include model selection on the evaluation data.

free parameters (5)
  • gamma = not reported
    Controls suppression strength in Eq (9); no value or selection method given, and it directly sets the behavior of the scaling.
  • tau = not reported
    Skip threshold in Algorithm 1; no value or selection method given; determines which updates are dropped.
  • learning_rate = CIFAR-10: 0.5; CIFAR-100: 0.1
    Selected from a grid by best test accuracy in Table 4, so test-set information enters the final model.
  • layer_count = 1
    Chosen based on Table 2 test accuracies, despite l=3 being best on CIFAR-10; used in all later runs.
  • qubit_count = 10
    Chosen from {2,3,5,10} based on Table 1 to balance accuracy and simulation cost; a design choice fitted to the datasets.
assumptions (5)
  • domain assumption The QFL objective is strongly convex and smooth so the bound in Eq (8) applies.
    Eq (8) is a standard strongly convex SGD bound; QNN classifiers are nonconvex and the paper does not justify these constants.
  • domain assumption Device noise is fully described by Pauli channels E(rho)=(1-epsilon)rho+epsilon*sum Ej rho Ej†.
    Used to model heterogeneous noise (Sec 3.3); real hardware also has amplitude damping, readout error, and crosstalk.
  • domain assumption The noise deviation xi has bounded variance as in Eq (7), with the stated constants.
    Eq (7) is stated without derivation and is the input to the convergence bound.
  • ad hoc to paper Each client can access |xi| to compute the sporadic variable.
    Algorithm 1 lines 8-9 and Eq (9) require the noise-free gradient to be known, which is not possible on a real device.
  • ad hoc to paper Scaling updates by x reduces the noise variance term V in Eq (8).
    Asserted in Sec 3.4 after Eq (10) with no derivation; the interaction between x, bias, and variance is not analyzed.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise." pith.science (2026). https://pith.science/paper/GJXCGCMQ

@misc{pith2026250712492,
  author       = {Pith},
  title        = {Pith review of: Sporadic Federated Learning Approach in Quantum Environment to Tackle Quantum Noise},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/GJXCGCMQ}},
  note         = {Machine review of arXiv:2507.12492}
}
read the original abstract

Quantum Federated Learning (QFL) is an emerging paradigm that combines quantum computing and federated learning (FL) to enable decentralized model training while maintaining data privacy over quantum networks. However, quantum noise remains a significant barrier in QFL, since modern quantum devices experience heterogeneous noise levels due to variances in hardware quality and sensitivity to quantum decoherence, resulting in inadequate training performance. To address this issue, we propose SpoQFL, a novel QFL framework that leverages sporadic learning to mitigate quantum noise heterogeneity in distributed quantum systems. SpoQFL dynamically adjusts training strategies based on noise fluctuations, enhancing model robustness, convergence stability, and overall learning efficiency. Extensive experiments on real-world datasets demonstrate that SpoQFL significantly outperforms conventional QFL approaches, achieving superior training performance and more stable convergence.

Figures

Figures reproduced from arXiv: 2507.12492 by the authors.

Figure 1
Figure 1. Proposed SpoQFL architecture where a set of distributed quantum devices collaborate with a quantum server to train a shared QML model. The proposed framework encompasses spo￾radic learning that captures noise heterogeneity and mitigates the effects of quantum noise in QFL. the classical FL, QFL allows numerous clients to train mod￾els without exchanging raw data, ensuring data privacy and lowering communication over… view at source ↗
Figure 2
Figure 2. Performance comparison between FL with classical model and FL with the quantum model for CIFAR-10 and CIFAR-100 dataset [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Performance comparison between QFL and SpoQFL for CIFAR-10 and CIFAR-100 dataset in IID and non-IID data distribution. For adding quantum noise, we set the noise level to ϵ = 0.001. Category Method CIFAR-10 CIFAR-100 Loss Value Acc. Loss Value Acc. Classical FL FedAvg [19] 1.2345 70.12% 2.0456 39.45% FedProx [18] 1.1876 72.34% 1.9876 40.87% Moon [17] 1.0984 74.56% 1.8765 42.78% PFL [26] 1.0567 76.89% 1.7890 45.12% Q… view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

37 extracted references · 33 canonical work pages

  1. [1]

    Fedopt: federated learning-based heteroge- neous resource recommendation and optimization for edge computing

    Syed Thouheed Ahmed, V Vinoth Kumar, TR Mahesh, LV Narasimha Prasad, AK Velmurugan, V Muthukumaran, and VR Niveditha. Fedopt: federated learning-based heteroge- neous resource recommendation and optimization for edge computing. Soft Computing, pages 1–12, 2024. 1, 2

    work page 2024

  2. [2]

    Is- sues in federated learning: some experiments and prelimi- nary results

    Jamsher Bhanbhro, Simona Nistic `o, and Luigi Palopoli. Is- sues in federated learning: some experiments and prelimi- nary results. Scientific Reports, 14(1):1–15, 2024. 1, 2

    work page 2024

  3. [3]

    Pancotti

    Jacob Biamonte, Peter Wittek, and Nicola et al. Pancotti. Quantum machine learning. Nature, 549:195–202, 2017. 2

    work page 2017

  4. [4]

    Quantum error mitigation

    Zhenyu Cai, Ryan Babbush, Simon C Benjamin, Suguru Endo, William J Huggins, Ying Li, Jarrod R McClean, and Thomas E O’Brien. Quantum error mitigation. Reviews of Modern Physics, 95(4):045005, 2023. 2

    work page 2023

  5. [5]

    Quantum federated learn- ing with quantum data

    Mahdi Chehimi and Walid Saad. Quantum federated learn- ing with quantum data. In ICASSP 2022-2022 IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP), pages 8617–8621. IEEE, 2022. 1, 7, 8

    work page 2022

  6. [6]

    Robust quan- tum federated learning with noise

    Liangjun Chen, Lili Yan, and Shibin Zhang. Robust quan- tum federated learning with noise. Physica Scripta, 99(7): 076003, 2024. 1, 2

    work page 2024

  7. [7]

    Mqfl- fhe: Multimodal quantum federated learning framework with fully homomorphic encryption

    Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia, Muham- mad Shafique, and David Esteban Bernal Neira. Mqfl- fhe: Multimodal quantum federated learning framework with fully homomorphic encryption. arXiv preprint arXiv:2412.01858, 2024. 2

    arXiv 2024

  8. [8]

    An enhanced approach for predicting air pollution using quantum support vector machine

    Omer Farooq, Maida Shahid, Shazia Arshad, Ayesha Altaf, Faiza Iqbal, Yini Airet Miro Vera, Miguel Angel Lopez Flo- res, and Imran Ashraf. An enhanced approach for predicting air pollution using quantum support vector machine. Scien- tific Reports, 14(1):19521, 2024. 1, 2

    work page 2024

Show all 37 references
  1. [9]

    A personalized quantum federated learning

    Dev Gurung and Shiva Raj Pokhrel. A personalized quantum federated learning. In Proceedings of the 8th Asia-Pacific Workshop on Networking, pages 175–176, 2024. 7, 8

    2024

  2. [10]

    Decentralized federated learning: A survey on security and privacy

    Ehsan Hallaji, Roozbeh Razavi-Far, Mehrdad Saif, Boyu Wang, and Qiang Yang. Decentralized federated learning: A survey on security and privacy. IEEE Transactions on Big Data, 10(2):194–213, 2024. 1

    2024

  3. [11]

    Mea- suring the effects of non-identical data distribution for feder- ated visual classification

    Tzu-Ming Harry Hsu, Hang Qi, and Matthew Brown. Mea- suring the effects of non-identical data distribution for feder- ated visual classification. arXiv preprint arXiv:1909.06335,

    1909 arXiv

  4. [12]

    Power of data in quantum machine learning

    Hsin-Yuan Huang, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, and Jarrod R McClean. Power of data in quantum machine learning. Na- ture communications, 12(1):2631, 2021. 1, 2

    2021

  5. [13]

    Error-mitigation- aided optimization of parameterized quantum circuits: Con- vergence analysis

    Sharu Theresa Jose and Osvaldo Simeone. Error-mitigation- aided optimization of parameterized quantum circuits: Con- vergence analysis. IEEE Transactions on Quantum Engi- neering, 3:1–19, 2022. 1

    2022

  6. [14]

    Scaffold: Stochastic controlled averaging for feder- ated learning

    Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for feder- ated learning. In International conference on machine learn- ing, pages 5132–5143. PMLR, 2020. 1, 2

    2020

  7. [15]

    Microcontroller-driven mppt system for enhanced photovoltaic efficiency: An experimen- tal approach in nepal

    Diwakar Khadka, Satish Adhikari, Atit Pokharel, Sandeep Marasinee, and Aayush Pathak. Microcontroller-driven mppt system for enhanced photovoltaic efficiency: An experimen- tal approach in nepal. arXiv preprint arXiv:2412.06956 ,

    arXiv

  8. [16]

    Learning multiple layers of features from tiny images.(2009), 2009

    Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images.(2009), 2009. 4

    2009

  9. [17]

    Model- contrastive federated learning

    Qinbin Li, Bingsheng He, and Dawn Song. Model- contrastive federated learning. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10713–10722, 2021. 2, 7, 8

    2021

  10. [18]

    Federated optimiza- tion in heterogeneous networks

    Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smith. Federated optimiza- tion in heterogeneous networks. Proceedings of Machine learning and systems, 2:429–450, 2020. 1, 7, 8

    2020

  11. [19]

    Communication- efficient learning of deep networks from decentralized data

    Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication- efficient learning of deep networks from decentralized data. In Artificial intelligence and statistics , pages 1273–1282. PMLR, 2017. 1, 2, 7, 8

    2017

  12. [20]

    Securing data privacy in ma- chine learning: The fedavg of federated learning approach

    Shiva Mehta and Aseem Aneja. Securing data privacy in ma- chine learning: The fedavg of federated learning approach. In 2024 4th Asian Conference on Innovation in Technology (ASIANCON), pages 1–5. IEEE, 2024. 1, 2

    2024

  13. [21]

    A tuto- rial on quantum convolutional neural networks (qcnn)

    Seunghyeok Oh, Jaeho Choi, and Joongheon Kim. A tuto- rial on quantum convolutional neural networks (qcnn). In 2020 International Conference on Information and Commu- nication Technology Convergence (ICTC) , pages 236–239. IEEE, 2020. 7, 8

    2020

  14. [22]

    Entanglement-controlled quan- tum federated learning

    Soohyun Park, Hyunsoo Lee, Soyi Jung, Jihong Park, Mehdi Bennis, and Joongheon Kim. Entanglement-controlled quan- tum federated learning. IEEE Internet of Things Journal ,

  15. [23]

    Quantum computing: Exploring superposition and entanglement for cutting-edge applications

    Sahib J Parmar, Vinitkumar R Parmar, Jaiprakash Verma, Satyabrata Roy, and Pronaya Bhattacharya. Quantum computing: Exploring superposition and entanglement for cutting-edge applications. In 2023 16th International Con- ference on Security of Information and Networks (SIN) , p...

    2023

  16. [24]

    Electrocardiogram (ecg) based cardiac arrhythmia detection and classification using machine learn- ing algorithms

    Atit Pokharel, Shashank Dahal, Pratik Sapkota, and Bhupen- dra Bimal Chhetri. Electrocardiogram (ecg) based cardiac arrhythmia detection and classification using machine learn- ing algorithms. arXiv preprint arXiv:2412.05583, 2024. 1

    2024 arXiv

  17. [25]

    Perfor- mance evaluation of lora technology for rural connectiv- ity: An experimental analysis in nepal

    Atit Pokharel, Pratik Sapkota, Dilip Sapkota, Shashank Dahal, Sulav Karki, and Ram Kaji Budhathoki. Perfor- mance evaluation of lora technology for rural connectiv- ity: An experimental analysis in nepal. arXiv preprint arXiv:2412.04563, 2024. 1

    2024 arXiv

  18. [26]

    Improved modula- tion recognition using personalized federated learning

    Ratun Rahman and Dinh C Nguyen. Improved modula- tion recognition using personalized federated learning. IEEE Transactions on Vehicular Technology, 2024. 1, 2, 7, 8

    2024

  19. [27]

    Multimodal federated learning with model personalization

    Ratun Rahman and Dinh C Nguyen. Multimodal federated learning with model personalization. In OPT 2024: Opti- mization for Machine Learning, 2024. 1

    2024

  20. [28]

    Electri- cal load forecasting in smart grid: A personalized federated learning approach

    Ratun Rahman, Neeraj Kumar, and Dinh C Nguyen. Electri- cal load forecasting in smart grid: A personalized federated learning approach. arXiv preprint arXiv:2411.10619, 2024. 1

    2024 arXiv

  21. [29]

    Electrical load forecasting over multihop smart metering networks with federated learning

    Ratun Rahman, Pablo Moriano, Samee U Khan, and Dinh C Nguyen. Electrical load forecasting over multihop smart metering networks with federated learning. arXiv preprint arXiv:2502.17226, 2025. 1

    2025

  22. [30]

    The quest for a quantum neural network

    Maria Schuld, Ilya Sinayskiy, and Francesco Petruccione. The quest for a quantum neural network. Quantum Infor- mation Processing, 13:2567–2586, 2014. 7, 8

    2014

  23. [31]

    Circuit-centric quantum classifiers

    Maria Schuld, Alex Bocharov, Krysta Svore, and Nathan Wiebe. Circuit-centric quantum classifiers. Physical Review A, 101(3):032308, 2019. 1, 2

    2019

  24. [32]

    Imple- mentation of error correction on ibm quantum computing de- vices

    Sangat Sharma, Suresh Basnet, and Raju Khanal. Imple- mentation of error correction on ibm quantum computing de- vices. Journal of Nepal Physical Society , 8(1):7–15, 2022. 1

    2022

  25. [33]

    Per- sonalized quantum federated learning for privacy image clas- sification

    Jinjing Shi, Tian Chen, Shichao Zhang, and Xuelong Li. Per- sonalized quantum federated learning for privacy image clas- sification. arXiv preprint arXiv:2410.02547, 2024. 7, 8

    2024 arXiv

  26. [34]

    False data injection attack detection in edge-based smart metering networks with federated learning

    Md Raihan Uddin, Ratun Rahman, and Dinh C Nguyen. False data injection attack detection in edge-based smart metering networks with federated learning. arXiv preprint arXiv:2411.01313, 2024. 1

    2024 arXiv

  27. [35]

    Quantumnat: quantum noise-aware training with noise injection, quantization and normalization

    Hanrui Wang, Jiaqi Gu, Yongshan Ding, Zirui Li, Frederic T Chong, David Z Pan, and Song Han. Quantumnat: quantum noise-aware training with noise injection, quantization and normalization. In Proceedings of the 59th ACM/IEEE design automation conference, pages 1–6, 2022. 2

    2022

  28. [36]

    On convergence of fedprox: Lo- cal dissimilarity invariant bounds, non-smoothness and be- yond

    Xiaotong Yuan and Ping Li. On convergence of fedprox: Lo- cal dissimilarity invariant bounds, non-smoothness and be- yond. Advances in Neural Information Processing Systems, 35:10752–10765, 2022. 2

    2022

  29. [37]

    Decentralized sporadic federated learning: A unified methodology with generalized convergence guarantees

    Shahryar Zehtabi, Dong-Jun Han, Rohit Parasnis, Seyyedali Hosseinalipour, and Christopher G Brinton. Decentralized sporadic federated learning: A unified methodology with generalized convergence guarantees. arXiv e-prints, pages arXiv–2402, 2024. 2

    2024

Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.