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REVIEW 5 major objections 5 minor 2 cited by

FedNAMs: Performing Interpretability Analysis in Federated Learning Context

T0 review · 5 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Federated learning gains per-feature explanations by training one small network per input feature and averaging the feature functions across clients.

desk verdict Routine composition of NAMs and FedAvg with no evaluation to back the central accuracy claim. read the letter →

arxiv 2506.17466 v1 pith:X3WO6DHH submitted 2025-06-20 cs.LG cs.AI

Amitash Nanda , Sree Bhargavi Balija , Debashis Sahoo This is my paper · ORCID
classification cs.LGcs.AI
keywords federatedlearningneuraladditivemodelsinterpretabilitygeneralizedfeatureattributionexplainableAIdistributedtrainingtabulardata
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

FedNAMs places a Neural Additive Model — one small network per input feature — inside a federated learning loop, so that each client learns its own shape function for every feature while data never leaves the device. The paper's central claim is that averaging these client shape functions pointwise yields a global model that is both accurate and interpretable, with only a small accuracy trade-off compared with ordinary federated deep networks. That matters because finance and healthcare need explanations of model decisions, and federated models have so far been mostly black boxes. On the Wine, Heart Disease, and Iris benchmarks, the framework identifies the same high-importance features (volatile acidity, sulfates, chlorides; chest pain type, maximum heart rate, number of vessels; petal length and width) at both client and global level.

What carries the argument

The load-bearing object is the per-feature shape function $f_i(x_k)$: each input feature is processed by its own small neural network (a FeatureNN with three hidden layers of 20 neurons and ExU units), and the federated global model is the pointwise average of the client shape functions (Eqs. 3-7), rather than an average of network weights as in FedAvg. This function-space averaging is what converts client-local additive models into a global additive model whose per-feature plots are directly readable.

What would settle it

Train FedNAMs on the same three benchmark datasets with a deliberately non-IID partition (e.g., each client receiving one class or one subpopulation) and compare the function-space-averaged global model's accuracy and per-feature curves against a centrally trained NAM on the pooled data; if the averaged model's accuracy or the shape of the curves diverges substantially, the aggregation rule does not hold.

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Extended reading notes

Core claim

The paper introduces Federated Neural Additive Models (FedNAMs), in which the global model is not a single shared network but an additive collection of per-feature networks. Each client $i$ trains its own shape functions $f^i_k(x_k)$ for every feature $k$, and the final global feature function is defined as the unweighted average of the client functions, $f_k(x_k) = \frac{1}{n}\sum_i f^i_k(x_k)$. Because each feature's contribution is a separate sub-network, the model retains the glass-box property of Generalized Additive Models, and the per-client structure yields client-level explanations alongside the global explanation. The paper reports that on UCI Heart Disease, OpenML Wine, Iris, and MNIST, FedNAMs achieve strong interpretability with minimal accuracy loss compared to traditional federated DNNs, and that the identified important features match domain expectations.

Load-bearing premise

The paper assumes that client-specific feature networks are comparable enough that averaging their per-feature outputs yields a valid global model, without aligning the networks or proving that the average preserves the accuracy and zero-centering of the individual shape functions.

Editorial extensions

If this is right

  • Federated models on tabular data can be glass-box: each feature's marginal effect is visible as a curve, both per client and globally.
  • Privacy is preserved at the level of standard federated learning, since only shape-function updates (or the functions themselves) leave clients, not raw data.
  • Low-contributing features can be identified and potentially dropped, streamlining models in federated deployment.
  • Client-level variation in shape functions provides a natural diagnosis of non-IID data drift.
  • The approach extends NAMs' multiclass and multitask flexibility to federated settings, beyond tree-based GAMs.

Reading between the lines

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

  • If function-space averaging of independently trained shape functions is stable, FedNAMs could serve as a cheap diagnostic layer on top of existing federated deep networks, rather than a replacement architecture.
  • A testable consequence the paper leaves implicit is that the global explanation should approach the explanation of a centrally trained NAM on pooled data as client data becomes IID, giving a calibration check for the aggregation step.
  • The same aggregation rule could be applied to other additive interpretable models (e.g., spline GAMs or boosted trees) inside federated loops, provided their feature functions are comparable across clients.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

5 major / 5 minor

Summary. The paper proposes FedNAMs, a federated learning framework that trains Neural Additive Models (NAMs) on each client and aggregates the per-feature shape functions by averaging them in function space. The authors claim that FedNAMs provide strong interpretability, identify critical predictive features at client and global levels, and achieve state-of-the-art performance with minimal accuracy loss relative to traditional federated DNNs, based on experiments on the UCI Heart Disease, OpenML Wine, and Iris datasets. The manuscript describes the architecture, presents client-wise feature contribution tables and shape-function plots, and qualitatively compares with Captum attributions on a masked MNIST image. It also includes a reproducibility artifact link and an appendix with additional plots.

Significance. If the claims were substantiated, FedNAMs would be a useful contribution to interpretable federated learning: per-feature, client-level explanations on tabular benchmarks without major accuracy loss would be practically valuable, especially in finance and healthcare. The architectural idea of imposing an additive structure in a federated setting and aggregating shape functions is reasonable and worth exploring. However, the paper's central empirical claims are entirely unsupported by the presented evidence: no predictive accuracy, ROC-AUC, variance, or baseline comparison appears anywhere, and the interpretability benchmark against Captum is not quantitatively validated. The paper's current value is therefore only as a proposal sketch, not as a demonstrated method.

major comments (5)
  1. [Section 6 (Experimentation and Results)] The evaluation section states that 'model performance is evaluated based on classification accuracy and metrics such as the ROC-AUC score,' but no accuracy, ROC-AUC, confidence interval, or baseline comparison is reported anywhere in the manuscript. Tables 1-3 list feature contribution values and Captum attributions, and Appendix B contains only shape-function plots and a masked MNIST image. The abstract's claim of 'minimal accuracy loss compared to traditional Federated Deep Neural Networks' and the conclusion's claim of 'state-of-the-art performance' are therefore unsupported by any quantitative evidence. This is a load-bearing omission: the paper's central contribution is empirical, and the promised evaluation is absent.
  2. [Equations (3)-(7)] The aggregation rule averages client-specific shape functions f_i(x_k) in function space to obtain the global feature function. In contrast, standard FedAvg, given in Equation (2), averages network weights. The manuscript provides no justification that per-feature networks across clients are semantically aligned such that averaging their outputs yields a meaningful global model. Issues such as permutation of hidden units, differing bias terms, non-identifiability of additive components (the E[f_i]=0 centering constraint is mentioned in Section 1 but not enforced or discussed in the federated setting), and lack of convergence guarantees are not addressed. Without an alignment argument or an experiment comparing function-space averaging to weight-space FedAvg, the validity of the 'final aggregated feature function' is not established, and this directly affects both accuracy and interpretability claims.
  3. [Tables 1-3 and Section 6.1] The interpretability benchmark against Captum is not quantitatively validated. Table 1 reports client-wise feature contributions on a scale of roughly 2-5, while Table 2 reports Captum attribution values on a scale of roughly -0.01 to 0.003; no correlation, rank agreement, or normalized comparison is given. The text claims that FedNAMs offer 'more detailed and feature-specific interpretability than Captum,' but no metric supports this comparative claim. Figure 6 is a qualitative image comparison. The assertion that the framework's interpretability is validated by this benchmark is therefore unsupported.
  4. [Table 3] Table 3 is captioned as 'Client-wise feature contributions and Feature attribution values of Captum for UCI Wine dataset with reduced precision,' but the table lists heart-disease features (thalach, thal, age, ca, cp, sex, trestbps, oldpeak, fbs, restecg, exang, slope, chol), not wine features such as fixed acidity, volatile acidity, or sulfates. This internal inconsistency invalidates the wine-specific feature contribution results and undermines the paper's claims about identifying critical predictive features for the wine dataset. The source of this error needs to be corrected and the results regenerated.
  5. [Abstract and Section 6.2] The abstract claims the paper studies 'various text and image classification tasks,' but the experiments use only tabular datasets (Heart Disease, Wine, Iris). The only image experiment is a qualitative MNIST illustration of masked pixels in Section 6.2, which is not a classification task with reported performance. This overstatement misrepresents the scope of the experiments and should be corrected.
minor comments (5)
  1. [Section 5 (Datasets)] The Iris dataset description says the target has three classes but names only 'Iris-setosa and Iris-versicolor'; the third class (Iris-virginica) is missing. Also, the text describes Iris classification as 'multi-label classification,' but it is a multiclass problem, not multi-label.
  2. [Section 2] There is a typo: 'model'sel's' should be 'model's'.
  3. [Equations (8)-(11)] The notation g(E[y_client1]) is unclear; it mixes client indices with a generic link function and does not define the relationship between the per-client equations and the aggregated functions in Equations (3)-(7). The superscripts in later equations also appear inconsistent (e.g., f_i^j versus f_j^i).
  4. [Table 3 caption] The phrase 'with reduced precision' is vague; it is unclear whether this refers to numerical precision, model precision, or something else, and no explanation is given.
  5. [References] Some references do not support the claims they are cited for. For example, Zhang et al. (2024a) is cited as 'proposed FedGNN, a federated learning framework that utilizes Graph Neural Networks,' but the listed reference is 'Recent methodological advances in federated learning for healthcare,' which does not describe FedGNN. The citation for Bonawitz (2019) also lacks page numbers and appears in an unusual format.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the FedNAM derivation is a direct NAM-plus-FedAvg construction; the unsupported accuracy claim is an evidentiary issue, not a circular one.

full rationale

The paper's core derivation is a direct construction: each client trains a NAM with per-feature networks, and Equations (3)-(7) define the global feature function as the average of client shape functions. Equations (8)-(11) then write each client's GAM as the sum of those feature functions. This is an explicit algorithmic aggregation scheme, not a hidden fit renamed as a prediction. The 'critical predictive features' (volatile acidity, chest pain type, petal length/width, etc.) are read off the fitted shape functions and client-wise contribution tables (Tables 1-3, Figures 4-5); that is descriptive interpretability of the trained model, which is normal practice in interpretability work, not circular derivation. The only self-citations (Balija et al. 2024; Nanda et al. 2024) appear in general background about language modeling and federated learning and are not load-bearing for the FedNAMs claims. The paper's serious weakness is evidentiary: Section 6 promises accuracy and ROC-AUC evaluation but reports no quantitative metrics, so the 'minimal accuracy loss' and 'state-of-the-art' statements in the abstract and conclusion are unsupported. That is a missing-evidence/correctness problem, not circularity. No step reduces by construction to its own input, so the circularity score is 0.

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

The framework is a composition of known components, NAMs and FedAvg. The main added assumptions are the architectural choice and the function-space averaging of client feature networks, neither of which is derived or independently validated.

free parameters (4)
  • NAM architecture depth and width = 3 hidden layers, 20 neurons each
    Section 3 states the NAM architecture; chosen by hand, not a derived or constrained quantity.
  • Number of clients = 3
    Section 6 fixes three clients; the reported feature contributions depend on this setup.
  • Hyperparameters (dropout, learning rate, batch size) = not reported
    Section 6 says grid search across clients but reports no selected values, so the exact runs cannot be reconstructed.
  • High versus low feature categorization threshold
    Figure 3 and Section 6 label features as high or low contributing without specifying a cutoff; the conclusions about critical features depend on this unspecified threshold.
assumptions (3)
  • domain assumption The target decomposes additively as g(E[y]) = beta + sum_i f_i(x_i)
    Section 3, Equation (1); the entire interpretability claim rests on the assumption that the target variable is explainable by separable per-feature functions.
  • domain assumption Averaging client updates via FedAvg yields a valid global model
    Equation (2) adopts the FedAvg update; the paper relies on the standard federated learning assumption that clients are comparable and weight averaging is effective.
  • ad hoc to paper Averaging per-feature functions across clients preserves semantic alignment
    Equations (3) through (7) average client shape functions without permutation alignment or matching; no guarantee is provided that these functions are aligned across clients.

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Cite this review

Pith. "Pith review of FedNAMs: Performing Interpretability Analysis in Federated Learning Context." pith.science (2026). https://pith.science/paper/X3WO6DHH

@misc{pith2026250617466,
  author       = {Pith},
  title        = {Pith review of: FedNAMs: Performing Interpretability Analysis in Federated Learning Context},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/X3WO6DHH}},
  note         = {Machine review of arXiv:2506.17466}
}
read the original abstract

Federated learning continues to evolve but faces challenges in interpretability and explainability. To address these challenges, we introduce a novel approach that employs Neural Additive Models (NAMs) within a federated learning framework. This new Federated Neural Additive Models (FedNAMs) approach merges the advantages of NAMs, where individual networks concentrate on specific input features, with the decentralized approach of federated learning, ultimately producing interpretable analysis results. This integration enhances privacy by training on local data across multiple devices, thereby minimizing the risks associated with data centralization and improving model robustness and generalizability. FedNAMs maintain detailed, feature-specific learning, making them especially valuable in sectors such as finance and healthcare. They facilitate the training of client-specific models to integrate local updates, preserve privacy, and mitigate concerns related to centralization. Our studies on various text and image classification tasks, using datasets such as OpenFetch ML Wine, UCI Heart Disease, and Iris, show that FedNAMs deliver strong interpretability with minimal accuracy loss compared to traditional Federated Deep Neural Networks (DNNs). The research involves notable findings, including the identification of critical predictive features at both client and global levels. Volatile acidity, sulfates, and chlorides for wine quality. Chest pain type, maximum heart rate, and number of vessels for heart disease. Petal length and width for iris classification. This approach strengthens privacy and model efficiency and improves interpretability and robustness across diverse datasets. Finally, FedNAMs generate insights on causes of highly and low interpretable features.

Figures

Figures reproduced from arXiv: 2506.17466 by the authors.

Figure 1
Figure 1. Neural additive model architecture in a federated learning setup [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Two different neural networks considered for text and image datasets. [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. High and low interpretable features and their causes are shown for the heart disease dataset. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Image depicting variation of output with respect to different features for Iris dataset [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Benchmark comparison with Meta’s captum (right) for highly contributing pixels (masked) [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

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

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Reference graph

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