Pith. sign in

REVIEW 4 major objections 5 minor 1 cited by

Decoding Federated Learning: The FedNAM+ Conformal Revolution

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read A federated learning pipeline using Neural Additive Models and a gradient-adjusted conformal predictor claims prediction sets with coverage guarantee and only 0.1% MNIST accuracy loss.

desk verdict A genuinely new gradient-based heuristic buried under an unsupported conformal coverage claim; the paper contradicts itself and should be rejected, though the DLA idea merits a proper rework. read the letter →

arxiv 2506.17872 v2 pith:5AN36GWB submitted 2025-06-22 cs.LG cs.CV

Sree Bhargavi Balija , Amitash Nanda , Debashis Sahoo This is my paper · ORCID
classification cs.LGcs.CV
keywords federatedlearningneuraladditivemodelsconformalpredictionuncertaintyquantificationdynamicleveladjustmentpixel-wisemodelinterpretabilitygradientsensitivity
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

The paper aims to make federated learning both interpretable and uncertainty-aware. It proposes FedNAM+, which combines Neural Additive Models with a conformal-prediction scheme called Dynamic Level Adjustment: gradient magnitudes from each input determine how wide each prediction set should be. The intended payoff is that a clinician or analyst sees not just a prediction but a pixel-level map of where the model is unsure, calibrated to a user-chosen coverage level, without the repeated forward passes that Monte Carlo Dropout requires. On MNIST the reported accuracy loss is 0.1%, and the method also produces class- and pixel-level uncertainty visualizations.

What carries the argument

The load-bearing mechanism is the additive decomposition of Neural Additive Models, $f_m(x) = \sum_j f_{m,j}(x_j)$, together with the Dynamic Level Adjustment algorithm. The NAM's separated per-feature networks make each pixel's contribution explicit, and the algorithm's per-sample scaling of $\alpha$ by normalized input-gradient magnitudes is what turns ordinary conformal sets into adaptive ones. This mechanism works by using gradient magnitude as a proxy for predictive uncertainty, then applying that proxy to adjust interval width while retaining the conformal calibration threshold computed on each client's data.

What would settle it

Run Algorithm 2 on a held-out test split with target 1-alpha = 0.9, compute empirical coverage across all clients, and check whether the observed coverage falls materially below 0.9 on a large calibration set; if it does, the coverage guarantee is not met and the central claim fails. A cleaner test uses synthetic data with known label noise, where the true uncertainty per sample is fixed, and checks whether the Dynamic Level Adjustment widths actually order samples by that noise.

Watch

Extended reading notes

Core claim

The central discovery claimed is an integration rather than a new theory: a federated global model, built by averaging client-trained parameters, is wrapped in a Neural Additive Model whose per-feature networks expose contributions, and a conformal predictor then produces prediction sets whose widths are modulated per sample by the input-gradient magnitude. Algorithm 2 normalizes gradient magnitudes, thresholds them at the median, and scales the base $\alpha$ by $\beta(1+G_{mag})$ on high-gradient samples and by $(1+G_{mag})$ otherwise. The claim is that these sets still satisfy the coverage guarantee $P[y_{n+1} \in C(x_{n+1})] \geq 1-\alpha$, while being narrower than quantile-regression conformal prediction's uniformly wide intervals and cheaper than Monte Carlo Dropout. The paper also claims this yields meaningful pixel-wise uncertainty estimates that identify low-confidence regions.

Load-bearing premise

The framework's central promise rests on the assumption that per-sample alpha values scaled by normalized gradient magnitudes still yield prediction sets meeting the stated coverage guarantee, which the paper's own limitations acknowledge is unproven.

Editorial extensions

If this is right

  • If the coverage guarantee holds, practitioners can choose a target like 90% or 95% and get prediction sets that include the true label at that rate on each client's data.
  • Uncertainty heatmaps become a byproduct of training, so clinicians can inspect which pixels drive a medical diagnosis and how confident the model is in that region.
  • The reported 0.1% MNIST accuracy drop means interpretability and uncertainty would come almost for free in accuracy terms, making the method attractive where Monte Carlo Dropout's repeated forward passes are too costly.
  • Class-level uncertainty summaries, such as digit 8 being most uncertain and digit 6 least, could guide targeted data collection for the classes the federated model handles worst.
  • Because the framework keeps data local and only exchanges weights, the uncertainty estimates are available without centralizing sensitive data.

Reading between the lines

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

  • A direct extension would replace the median-gradient threshold in Algorithm 2 with a held-out calibration split, so the coverage guarantee of Equation (1) is restored by construction rather than assumed; this is not what the paper proves.
  • If gradient magnitude is a reliable uncertainty signal, the same adjustment could be ported to non-additive architectures, giving adaptive conformal sets for any differentiable model without retraining.
  • The per-sample scaled sets could be merged across clients by aggregating nonconformity scores rather than weights, which would make the global coverage statement more robust to client heterogeneity; the paper does not test this.
  • The gap between gradient-based and Bayesian uncertainty in the paper's own table suggests the two measures are not interchangeable, so a practitioner should treat these intervals as relative confidence scores unless they are empirically calibrated on held-out labels.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 5 minor

Summary. The manuscript proposes FedNAM+, a federated learning framework that combines Neural Additive Models (NAMs) with a 'Dynamic Level Adjustment' (DLA) mechanism claimed to be a novel conformal prediction method. It asserts that the framework produces prediction sets satisfying the conformal coverage guarantee P[y_{n+1} in C(x_{n+1})] >= 1-alpha (Eq. 1), while providing pixel-level interpretability and computational efficiency relative to Monte Carlo Dropout. The experimental sections report results on CT-scan, MNIST, and CIFAR data, with visual comparisons of uncertainty intervals and feature attributions. The paper includes appended critical sections (Sections 11-13) that explicitly state that DLA lacks a theoretical foundation, that the convergence proof assumes IID data, and that the conformal prediction application is questionable. The central algorithmic object, Algorithm 2, adapts interval widths from gradient magnitudes without constructing non-conformity scores or calibration sets.

Significance. If the claimed coverage guarantee and efficiency results were valid, a method that combines interpretable NAMs with distribution-free prediction sets in federated learning would be a useful contribution. The paper does not, however, ship machine-checked proofs, reproducible code, or calibration-based coverage measurements, and the manuscript's own appended sections concede that the theoretical basis for the core method is missing. The DLA scheme may be worth exploring as an empirical heuristic for adaptive interval widths, but as presented it is not a conformal prediction method and does not establish the paper's central claims.

major comments (4)
  1. [Section 3.5, Algorithm 2] Algorithm 2 does not implement conformal prediction and cannot inherit the coverage guarantee in Eq. (1). Lines 5 through 9 compute per-sample gradient magnitudes, normalize them, compare them to the median, and return interval widths C = alpha*beta*(1+Gmag) or alpha*(1+Gmag). No non-conformity score S(x,y) is computed, no calibration set D_cal is formed, no empirical quantile Q_{1-alpha} is taken, and no exchangeability argument is supplied; the output is a scalar width rather than a prediction set over labels. Because alpha is modified per sample, the target coverage level changes pointwise, so the resulting intervals have no stated coverage level. Section 11 explicitly concedes that there is no formal proof correlating gradient magnitudes with reliable uncertainty estimates, and Section 13 states that the conformal application casts doubt on the validity of the results. The central claim of calibrated conformal coverage is therefore unsupported.
  2. [Section 3.2.1 and Section 12] The convergence proof is tautological. The asserted inequality L(w_{t+1}) <= L(w_t) - eta*||grad L(w_t)||^2 + O(delta_t) introduces O(delta_t) as the residual of exactly this inequality, so the statement cannot fail and provides no information about convergence or about the effect of Dynamic Level Adjustment. Section 12 itself states that the proof assumes IID client data, which is inconsistent with the federated setting and with the paper's own Section 8 limitation. No non-IID convergence analysis or bound on delta_t is provided. This theorem does not provide a theoretical foundation for FedNAM+.
  3. [Section 4.2 and Table 2] Coverage is listed among the evaluation metrics, but no experiment reports empirical coverage. Table 1 reports average uncertainties, Table 2 reports accuracy and prediction bounds, and Figures 3-9 display interval widths and pixel maps. The abstract's claim that the method achieves desired coverage levels is therefore unverified; the key quantitative guarantee of the paper is never measured.
  4. [Table 2 and Section 5] The abstract states a 'small drop in accuracy (0.1%) for MNIST,' but Table 2 reports model accuracy before the framework as 0.8734 and after as 0.8521, a drop of about 2.1 percentage points. The discrepancy is not explained. In addition, Algorithm 1 line 11 calls an undefined function ConformalPrediction and never constructs a calibration set or specifies how prediction sets are formed, so the experimental procedure does not match the claimed method.
minor comments (5)
  1. [Section 3.2.2] The claimed complexity reduction to O(N log N + delta) is asserted without derivation; the median computation in Algorithm 2 can explain the N log N term, but because no conformal procedure is specified, the claim is not meaningful.
  2. [Section 9.1 and Section 4.1] The ablation studies refer to a clustering mechanism that is not otherwise described, and Section 4.1 lists the UCI Diabetes dataset although no experiment on it appears in the results.
  3. [Sections 10-13] The manuscript contains an empty Section 10 titled 'Theoretical Contributions' followed by Sections 11-13 that acknowledge the lack of theoretical foundation, the IID assumption problem, and the questionable conformal application. If these sections are intended as limitations, they are inconsistent with the paper's claims; if they are intended as notes to reviewers, they should be removed or fully integrated into a revised framing.
  4. [References] Several citations appear mismatched or implausible: reference [20] cites Simonyan and Zisserman for EfficientNet, which is not the correct source, and reference [6] uses placeholder-style author names. The manuscript should be checked for citation accuracy.
  5. [Throughout] There are numerous typographical and grammatical errors (e.g., 'uncertainity', 'Ciph ar10', 'LIME ... falls short in estimating uncertainty') that require careful proofreading.

Circularity Check

3 steps flagged · score 8.0 of 10

The central convergence proof and conformal coverage claim reduce to definitional assertions: O(δ_t) is defined as the residual of the inequality, and Algorithm 2's intervals are defined directly from gradient magnitudes with no calibration quantile, so Eq. (1) is not derived.

  1. self definitional [Section 3.2.1, Theorem 1 (Proof of Convergence of FedNAM+ Using Dynamic Level Adjustment)]
    "By employing the Dynamic Level Adjustment Method, we obtain: L(wt+1)≤L (wt)−η∥∇L(wt)∥2 +O(δt), where δt denotes the adaptive adjustment factor introduced by the Dynamic Level Adjustment Method. The term O(δt) captures the adaptive learning adjustments that expedite convergence while maintaining stability."

    The inequality is true by construction: for any sequence of weights and any algorithm, one can define O(δt) = L(wt+1) − L(wt) + η∥∇L(wt)∥2, which makes the displayed bound an identity. The 'theorem' never specifies an update rule for wt+1, never relates δt to Algorithm 2's gradient-magnitude adjustment, and never derives the inequality from L-smoothness or client homogeneity. The proof therefore cannot fail and carries no information about FedNAM+; it is a renaming of the residual rather than a derived convergence result.

  2. fitted input called prediction [Section 3.5 and Algorithm 2 (Dynamic Level Adjustment)]
    "The conformal prediction algorithm uses a non-conformity score S(x,y) ... The calibration set Dcal at each client is used to compute a threshold τ, such that: τ =Q1−α({S(x,y)}(x,y)∈Dcal) ... Algorithm 2 ... Compute the gradient magnitude for each sample: Gmag← mean(|G|, axis=1) ... Adjust intervals dynamically: C←(α·β·(1+Gmag) if Gmag > Threshold; α·(1+Gmag) otherwise). Return conformal prediction intervals C."

    The operational definition of C in Algorithm 2 is exclusively a function of gradient magnitudes, α, β, and a median threshold. Nowhere does the algorithm construct a calibration set Dcal, compute non-conformity scores S(x,y), or evaluate the empirical quantile τ that split conformal prediction requires. The paper nevertheless labels the output 'conformal prediction intervals C' and attributes to it the coverage guarantee of Eq. (1). Since no calibration scores or quantiles appear, the coverage property cannot be inherited from conformal theory; it is an asserted property of a quantity defined as a gradient-magnitude rescaling.

1 more flagged steps
  1. other [Section 11 (Dynamic Level Adjustment Method) and Section 13 (Application of Conformal Prediction)]
    "Specifically, there is no formal proof that correlates gradient magnitudes with reliable uncertainty estimates. Therefore, the application of DLA in this context is questionable and requires further theoretical development. ... The implementation of conformal prediction methods in FedNAM+ lacks proper citation of foundational works and demonstrates a limited understanding of the technique."

    These passages are the paper's own acknowledgement that the load-bearing step between the method's input (gradient magnitudes) and its claimed output (reliable uncertainty satisfying a conformal guarantee) is missing. In circularity terms, the 'prediction' of calibrated coverage is not a consequence of the algorithm is defined; it is imposed on the output by calling the gradient-based heuristic a conformal method. Section 13 compounds this by stating that the conformal implementation lacks the foundational methodology that would supply the guarantee, so the central claim rests on an unproved identification rather than a derivation.

full rationale

The paper's main derivation chain is not self-contained. The convergence proof in Section 3.2.1 is a tautology: O(δt) is defined as whatever is needed to make the inequality hold, so it provides no independent support for FedNAM+. The conformal coverage claim is the central advertised contribution, but Algorithm 2 never computes a non-conformity score or a calibration quantile; its prediction intervals are defined directly from per-sample gradient magnitudes. Therefore the guarantee P[y_{n+1} ∈ C(x_{n+1})] ≥ 1−α in Eq. (1) is not derived from any conformal result, and the manuscript's Section 11 explicitly states that no proof links gradient magnitudes to reliable uncertainty. There is some independent empirical content, such as the reported accuracy numbers and visualizations, but those do not rescue the theoretical claim. Self-citations to the authors' prior FedNAM work appear, but they are not the main source of circularity here; the circularity is that the method's defining equations are presented as if they yielded properties that actually must be assumed. The correct score is high because the paper's headline result, calibrated conformal coverage, reduces by construction to a gradient-magnitude rescaling mislabeled as conformal prediction, and the convergence theorem is an identity in disguise.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The central claim of calibrated uncertainty rests on four unspecified or hand-picked parameters (alpha, beta, epsilon, delta_t), the IID assumption the paper itself disclaims in Section 8, and an unproven link between gradient magnitude and uncertainty that Section 11 explicitly concedes is missing. The conformal coverage guarantee is therefore not purchased by any theorem or measurement in the paper.

free parameters (4)
  • Base interval level alpha = not reported
    Input to Algorithm 2; the value is hand-chosen and never stated or swept for sensitivity.
  • Confidence boost beta = not reported
    Multiplicative factor for high-gradient samples in Algorithm 2; no value or tuning procedure is given.
  • Small constant epsilon = not reported
    Used to avoid division by zero in the min-max normalization of gradient magnitudes; exact value not specified.
  • Adaptive adjustment factor delta_t = not reported
    Appears in the convergence bound of Theorem 1 as O(delta_t); it is never defined or measured, making the inequality true by construction.
assumptions (3)
  • domain assumption The global loss L(w) is L-smooth and client data distributions are homogeneous or IID.
    Invoked in Theorem 1 (Section 3.2.1). The paper's own Section 8 admits that 'the methodology assumes that client datasets are Independent and Identically Distributed (IID),' which contradicts the federated non-IID setting.
  • standard math Exchangeability of calibration and test data is required for conformal prediction coverage.
    Conformal prediction (Eq. 1) relies on exchangeability. Algorithm 2 modifies alpha per sample using the test input's own gradient, breaking this requirement.
  • ad hoc to paper Gradient magnitude with respect to input is a valid proxy for predictive uncertainty.
    This is the central assumption of the DLA method. The paper's Section 11 explicitly states there is 'no formal proof that correlates gradient magnitudes with reliable uncertainty estimates.'

how reviews work

0 comments
Cite this review

Pith. "Pith review of Decoding Federated Learning: The FedNAM+ Conformal Revolution." pith.science (2026). https://pith.science/paper/5AN36GWB

@misc{pith2026250617872,
  author       = {Pith},
  title        = {Pith review of: Decoding Federated Learning: The FedNAM+ Conformal Revolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5AN36GWB}},
  note         = {Machine review of arXiv:2506.17872}
}
read the original abstract

Federated learning has significantly advanced distributed training of machine learning models across decentralized data sources. However, existing frameworks often lack comprehensive solutions that combine uncertainty quantification, interpretability, and robustness. To address this, we propose FedNAM+, a federated learning framework that integrates Neural Additive Models (NAMs) with a novel conformal prediction method to enable interpretable and reliable uncertainty estimation. Our method introduces a dynamic level adjustment technique that utilizes gradient-based sensitivity maps to identify key input features influencing predictions. This facilitates both interpretability and pixel-wise uncertainty estimates. Unlike traditional interpretability methods such as LIME and SHAP, which do not provide confidence intervals, FedNAM+ offers visual insights into prediction reliability. We validate our approach through experiments on CT scan, MNIST, and CIFAR datasets, demonstrating high prediction accuracy with minimal loss (e.g., only 0.1% on MNIST), along with transparent uncertainty measures. Visual analysis highlights variable uncertainty intervals, revealing low-confidence regions where model performance can be improved with additional data. Compared to Monte Carlo Dropout, FedNAM+ delivers efficient and global uncertainty estimates with reduced computational overhead, making it particularly suitable for federated learning scenarios. Overall, FedNAM+ provides a robust, interpretable, and computationally efficient framework that enhances trust and transparency in decentralized predictive modeling.

Figures

Figures reproduced from arXiv: 2506.17872 by the authors.

Figure 1
Figure 1. Neural additive models. Client 1 Client 2 Client 3 Client 4 Global Model weights weights weights weights [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. FedNAM+ architecture. 3.2.2. Lemma: Computational Complexity of the Con￾formal Prediction Method with Dynamic Adjust￾ments Let M represent the number of model evaluations, N de￾note the number of data points, and δ represent the extra computational cost associated with dynamic adjustments. The computational complexity of Monte Carlo Dropout is: O(M · N). By employing the Dynamic Level Adjustment Method, the conforma… view at source ↗
Figure 3
Figure 3. Comparison of Interval Informativeness: QCRP vs. Dy [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figures from the paper (6 more)
Figure 5
Figure 5. Figure 5: Average Uncertainty Across Predicted Classes for Ci [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]
Figure 4
Figure 4. Figure 4: Visualization of Uncertainty Intervals, Confidence Lev [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]
Figure 6
Figure 6. Figure 6: Visualization of Uncertainty Intervals, Confidence Lev [PITH_FULL_IMAGE:figures/full_fig_p007_6.png]
Figure 7
Figure 7. Figure 7: Interpretation of top 30% uncertainity pixels for CT scan [PITH_FULL_IMAGE:figures/full_fig_p007_7.png]
Figure 8
Figure 8. Figure 8: Interpretation of top 30% uncertainity pixels for CT scan [PITH_FULL_IMAGE:figures/full_fig_p007_8.png]
Figure 9
Figure 9. Figure 9: Comparison of Uncertainty Pixels Between the Fed [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]

Review history (2 revisions) →

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. The Trust Fabric: Decentralized Interoperability and Economic Coordination for the Agentic Web

    cs.CR 2025-07 reject novelty 3.0 of 10

    The paper presents a five-layer decentralized framework (Nanda) for agent discovery, trust scoring, and micropayments, but supports its deployment claims only with self-referential descriptions.

Reference graph

Works this paper leans on

37 extracted references · 32 canonical work pages · cited by 1 Pith paper

  1. [6]

    Cipahr: A secure federated framework for healthcare

    Jane Doe and Alice Smith. Cipahr: A secure federated framework for healthcare. In Proceedings of the IEEE Con- ference on Secure AI, 2022. 4

    work page 2022

  2. [1]

    Neural additive models: Interpretable machine learning with neural nets

    Rishabh Agarwal, Levi Melnick, Nicholas Frosst, Xuezhou Zhang, Ben Lengerich, Rich Caruana, and Geoffrey E Hin- ton. Neural additive models: Interpretable machine learning with neural nets. Advances in Neural Information Processing Systems, 34:4699–4711, 2021. 1, 2

    work page 2021

  3. [2]

    Handling privacy-sensitive medical data with federated learning: challenges and future directions

    Ons Aouedi, Alessio Sacco, Kandaraj Piamrat, and Guido Marchetto. Handling privacy-sensitive medical data with federated learning: challenges and future directions. IEEE Journal of Biomedical and Health Informatics , 27(2):790– 803, 2022. 1

    work page 2022

  4. [3]

    Fedmm-x: A trustworthy and inter- pretable framework for federated multi-modal learning in dynamic environments

    Sree Bhargavi Balija. Fedmm-x: A trustworthy and inter- pretable framework for federated multi-modal learning in dynamic environments. arXiv preprint arXiv:2503.19564 ,

    arXiv

  5. [4]

    Building communication efficient asynchronous peer-to-peer federated llms with blockchain

    Sree Bhargavi Balija, Amitash Nanda, and Debashis Sahoo. Building communication efficient asynchronous peer-to-peer federated llms with blockchain. In Proceedings of the AAAI Symposium Series, pages 288–292, 2024. 2

    work page 2024

  6. [5]

    Principal uncertainty quantifica- tion with spatial correlation for image restoration problems

    Omer Belhasin, Yaniv Romano, Daniel Freedman, Ehud Rivlin, and Michael Elad. Principal uncertainty quantifica- tion with spatial correlation for image restoration problems. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 44(8):4329–4344, 2021. 2

    work page 2021

  7. [7]

    Geyer, Tassilo Klein, and Moin Nabi

    Robin C. Geyer, Tassilo Klein, and Moin Nabi. Differentially private federated learning: A client-level perspective. arXiv preprint arXiv:1712.07557, 2017. 2

    arXiv 2017

  8. [8]

    Ailuminate: Introducing v1.0 of the ai risk and reliability benchmark from mlcommons

    Shaona Ghosh, Heather Frase, and Sree bhargavi balija Williams. Ailuminate: Introducing v1.0 of the ai risk and reliability benchmark from mlcommons. arXiv preprint arXiv:2503.05731, 2025. Submitted on 19 Feb 2025. 2

    arXiv 2025

Show all 37 references
  1. [9]

    Chest ct-scan images dataset

    Mohamed Hany. Chest ct-scan images dataset. Kaggle,

  2. [10]

    Seman- tic uncertainty: Linguistic invariances for uncertainty esti- mation in natural language generation

    Lorenz Kuhn, Yarin Gal, and Sebastian Farquhar. Seman- tic uncertainty: Linguistic invariances for uncertainty esti- mation in natural language generation. Journal of Machine Learning Research, 24(123):1–45, 2023. 3

    2023

  3. [11]

    Towards interpretable federated learning

    Anran Li, Rui Liu, Ming Hu, Luu Anh Tuan, and Han Yu. Towards interpretable federated learning. arXiv preprint arXiv:2302.13473, 2023. 1

    2023 arXiv

  4. [12]

    Heterogeneous federated learning

    Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith. Heterogeneous federated learning. Advances in Neu- ral Information Processing Systems, 33:10089–10100, 2020. 2

    2020

  5. [13]

    Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Str ¨umke, Vince I

    Luis M. Lopez-Ramos, Florian Leiser, Aditya Rastogi, Steven Hicks, Inga Str ¨umke, Vince I. Madai, Tobias Budig, Ali Sunyaev, and Adam Hilbert. Interplay between federated learning and explainable artificial intelligence: A scoping re- view. ACM Computing Surveys, 55(6):1–36, 2022. 2

    2022

  6. [14]

    Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas

    H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. Communication- efficient learning of deep networks from decentralized data. Artificial Intelligence and Statistics, pages 1273–1282, 2017. 2

    2017

  7. [15]

    On uncertainty quantification in language models

    John Michael. On uncertainty quantification in language models. Journal of AI Research, 2021. 3

    2021

  8. [16]

    Uncertainty quantification

    Johnson Michael. Uncertainty quantification. AI Review, 12 (7):1660, 2023. 3

    2023

  9. [17]

    Cptquant–a novel mixed precision post-training quantiza- tion techniques for large language models

    A Nanda, Sree Bhargavi Balija, and Debashis Sahoo. Cptquant–a novel mixed precision post-training quantiza- tion techniques for large language models. arXiv preprint arXiv:2506.xxxxx, 2025. 2

    2025

  10. [18]

    Fed- nams: Performing interpretability analysis in federated learning context

    A Nanda, Sree Bhargavi Balija, and Debashis Sahoo. Fed- nams: Performing interpretability analysis in federated learning context. arXiv preprint arXiv:2506.17466 , 2025. 2

    2025 arXiv

  11. [19]

    ”why should i trust you?”: Explaining the predictions of any classifier

    Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. ”why should i trust you?”: Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD In- ternational Conference on Knowledge Discovery and Data Mining, pages 1135–1144, 2016. 1

    2016

  12. [20]

    Very deep convo- lutional networks for large-scale image recognition

    Karen Simonyan and Andrew Zisserman. Very deep convo- lutional networks for large-scale image recognition. Interna- tional Conference on Learning Representations, 2015. 4

    2015

  13. [21]

    Federated multi-task learning

    Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar. Federated multi-task learning. Advances in Neural Information Processing Systems, 30, 2017. 2

    2017

  14. [22]

    Im- proved conformalized quantile regression

    Martim Sousa, Ana Maria Tom ´e, and Jos ´e Moreira. Im- proved conformalized quantile regression. Journal of Ma- chine Learning Research, 23(123):1–45, 2022. 2

    2022

  15. [23]

    Vision transformers for uncertainty estima- tion

    Tanya Verma. Vision transformers for uncertainty estima- tion. arXiv preprint arXiv:2212.34567, 2022. 4

    2022

  16. [24]

    Understand- ing neural abstractive summarization models via uncertainty

    Jiacheng Xu, Shrey Desai, and Greg Durrett. Understand- ing neural abstractive summarization models via uncertainty. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6305–6311, 2020. 3

    2020

  17. [25]

    A clustered federated learn- ing method of user behavior analysis based on non-iid data

    Jianfei Zhang and Zhongxin Li. A clustered federated learn- ing method of user behavior analysis based on non-iid data. Electronics, 12(7):1660, 2023. 1

    2023

  18. [26]

    A survey of trustworthy federated learning: Issues, solutions, and challenges

    Yifei Zhang, Dun Zeng, Jinglong Luo, Xinyu Fu, Guanzhong Chen, Zenglin Xu, and Irwin King. A survey of trustworthy federated learning: Issues, solutions, and challenges. ACM Transactions on Intelligent Systems and Technology , 15(1): 1–36, 2024. 2

    2024

  19. [29]

    Appendix 9.1. Ablation Studies To better understand the contributions of individual compo- nents in FedNAM+, we conducted a series of ablation stud- ies by systematically altering or removing specific features of the model and evaluating their impact on performance. The key ex...

  20. [30]

    • This resulted in a significant drop in interpretability and a minor decrease in accuracy, highlighting the crit- ical role of NAMs in providing explainable outcomes

    Removal of Neural Additive Models (NAMs): • In this experiment, NAMs were replaced with simpler linear models at each client level. • This resulted in a significant drop in interpretability and a minor decrease in accuracy, highlighting the crit- ical role of NAMs in providing...

  21. [31]

    • Improper tuning led to slower convergence and re- duced accuracy, emphasizing the importance of care- ful parameter selection

    Impact of Hyperparameter Tuning: • The effect of varying key hyperparameters, such as learning rate and regularization strength, was ana- lyzed. • Improper tuning led to slower convergence and re- duced accuracy, emphasizing the importance of care- ful parameter selection

  22. [32]

    Non-Clustering: • The clustering mechanism used in FedNAM+ was dis- abled, resulting in a noticeable decrease in computa- tional efficiency and a slight accuracy reduction

    Clustering vs. Non-Clustering: • The clustering mechanism used in FedNAM+ was dis- abled, resulting in a noticeable decrease in computa- tional efficiency and a slight accuracy reduction. • This showcases its role in balancing performance and efficiency

  23. [33]

    • The results indicated that the chosen aggregation ap- proach in FedNAM+ provides better alignment across clients, especially in IID scenarios

    Effect of Aggregation Techniques: • The study compared the current aggregation strategy with simpler averaging methods. • The results indicated that the chosen aggregation ap- proach in FedNAM+ provides better alignment across clients, especially in IID scenarios. 9.2. Results...

  24. [34]

    Theoretical Contributions

  25. [35]

    How- ever, this approach lacks a theoretical foundation guaran- teeing its validity

    Dynamic Level Adjustment (DLA) Method The DLA method proposes adjusting confidence levels based on gradient magnitudes to estimate uncertainty. How- ever, this approach lacks a theoretical foundation guaran- teeing its validity. Specifically, there is no formal proof that corr...

  26. [36]

    This as- sumption is inconsistent with the federated learning setting, where data is typically non-IID due to variations in local data distributions among clients

    Convergence Proof in Section 3.1 The convergence proof presented in Section 3.1 of the Fed- NAM+ documentation assumes that data across clients are independently and identically distributed (IID). This as- sumption is inconsistent with the federated learning setting, where dat...

  27. [37]

    Conformal prediction is a well-established method for pro- viding calibrated uncertainty estimates and relies on spe- cific assumptions and methodologies

    Application of Conformal Prediction The implementation of conformal prediction methods in FedNAM+ lacks proper citation of foundational works and demonstrates a limited understanding of the technique. Conformal prediction is a well-established method for pro- viding calibrated...

  28. [2020]

    Accessed: 2025-01-23. 4, 7

    2025

  29. [2025]

    Submitted on 25 Mar 2025. 1

    2025

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.