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arxiv: 2606.31742 · v1 · pith:VC7Q2MZ7new · submitted 2026-06-30 · 💻 cs.LG · cs.AI

FedXDS: Leveraging Model Attribution Methods to counteract Data Heterogeneity in Federated Learning

Maximilian Andreas Hoefler , Karsten Mueller , Wojciech Samek This is my paper

Pith reviewed 2026-07-01 05:57 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords federated learningdata heterogeneityfeature attributionexplainable AIselective data sharingmetric privacymembership inference
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The pith

Feature attribution from a single backward pass identifies which client data elements to share selectively, aligning distributions in federated learning while adding metric privacy protection.

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

The paper establishes that explainable AI attribution methods can determine precise data elements for controlled sharing across clients in federated learning, addressing the performance drop caused by statistical heterogeneity without raw data exposure. This matters to a sympathetic reader because federated setups often fail to converge well when client datasets differ in distribution, yet full data exchange violates privacy constraints. The approach computes task-relevant features via propagation-based attribution in one pass and applies a metric privacy layer to bound information leakage while retaining utility. Experiments across client counts and heterogeneity levels show gains in accuracy and speed over existing methods. The work also supplies formal privacy proofs and tests resistance to membership inference and feature inversion attacks.

Core claim

FedXDS is the first method to apply propagation-based attribution to select task-relevant features for selective inter-client sharing in federated learning, combined with metric privacy to enforce formal guarantees, yielding higher accuracy and faster convergence under heterogeneity compared to prior techniques.

What carries the argument

Propagation-based feature attribution computed in a single backward pass, used to select data elements for sharing under a metric privacy layer that preserves utility.

If this is right

  • Models reach higher test accuracy under the same number of communication rounds when client data distributions are aligned through attribution-guided sharing.
  • Convergence occurs in fewer rounds across both small and large client populations and across mild to severe heterogeneity.
  • The metric privacy layer supplies theoretical bounds that hold while empirical utility remains competitive with non-private baselines.
  • The resulting models exhibit lower success rates for membership inference and feature inversion attacks than models trained without the privacy layer.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same attribution step could be reused at later training stages to adaptively rebalance sharing as client distributions shift.
  • If the single-pass computation scales linearly, the overhead of attribution becomes negligible relative to standard federated averaging rounds.
  • The technique suggests a route to make data selection in other privacy-sensitive distributed training settings more interpretable rather than purely heuristic.
  • Robustness results against two specific attacks leave open whether the same privacy layer protects against other reconstruction or poisoning threats.

Load-bearing premise

That propagation-based attribution computed in a single backward pass can reliably identify precisely which data elements, when selectively shared, will mitigate statistical heterogeneity without compromising the privacy-utility tradeoff provided by the metric privacy layer.

What would settle it

A controlled experiment on standard heterogeneous federated datasets in which FedXDS produces no measurable gain in final accuracy or convergence rate relative to strong baselines, or in which membership inference or feature inversion attacks succeed at rates exceeding the claimed bounds.

Figures

Figures reproduced from arXiv: 2606.31742 by Karsten Mueller, Maximilian Andreas Hoefler, Wojciech Samek.

Figure 1
Figure 1. Figure 1: Illustration of the DP attribution-guided feature extrac [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Evaluation of privacy-utility trade-offs and hyperparameter effects in our framework. [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Inversion Attack on FedXDS [PITH_FULL_IMAGE:figures/full_fig_p015_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Inversion Attack on Raw Features deemed locally important—LRP is designed to propagate relevance backward through each layer of the network. This layer-wise relevance propagation produces coherent, con￾tiguous attributions. Consequently, at high sparsity (e.g., s = 85), the retained pixel regions form semantically consistent patches rather than scattered points, preserv￾ing enough context to maintain robus… view at source ↗
Figure 5
Figure 5. Figure 5: Inversion Attack on FedFed (a) Original Images (b) Protected Features (c) Denoising Attack Result [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Visualization of the privacy attack on protected features. Left: original images; Middle: protected features using Layer-wise [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗
read the original abstract

Explainable AI (XAI) methods have demonstrated significant success in recent years at identifying relevant features in input data that drive deep learning model decisions, enhancing interpretability for users. However, the potential of XAI beyond providing model transparency has remained largely unexplored in adjacent machine learning domains. In this paper, we show for the first time how XAI can be utilized in the context of federated learning. Specifically, while federated learning enables collaborative model training without raw data sharing, it suffers from performance degradation when client data distributions exhibit statistical heterogeneity. We introduce FedXDS (Federated Learning via XAI-guided Data Sharing), the first approach to utilize feature attribution techniques to identify precisely which data elements should be selectively shared between clients to mitigate heterogeneity. By employing propagation-based attribution, our method identifies task-relevant features through a single backward pass, enabling selective data sharing that aligns client contributions. To protect sensitive information, we incorporate metric privacy techniques that provide formal privacy guarantees while preserving utility. Experimental results demonstrate that our approach consistently achieves higher accuracy and faster convergence compared to existing methods across varying client numbers and heterogeneity settings. We provide theoretical privacy guarantees and empirically demonstrate robustness against both membership inference and feature inversion attacks. Code is available at https://github.com/MaxH1996/FedXDS.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces FedXDS, the first method to apply propagation-based XAI attribution (single backward pass) to identify task-relevant data elements for selective sharing across clients in federated learning, aiming to mitigate statistical heterogeneity while adding a metric privacy layer for formal guarantees. It claims superior accuracy and faster convergence versus baselines across client counts and heterogeneity levels, plus empirical robustness to membership inference and feature inversion attacks.

Significance. If the central empirical claims hold, the work offers a novel bridge between XAI and FL for non-IID data, with the code release aiding reproducibility. The privacy guarantees and attack robustness, if substantiated, would strengthen the contribution. However, the load-bearing assumption that attribution reliably selects heterogeneity-mitigating elements remains untested in the provided description.

major comments (2)
  1. [Method] The method relies on the claim that a single backward-pass propagation attribution identifies precisely the data elements whose selective sharing reduces cross-client shift. This is load-bearing for the heterogeneity-mitigation claim, yet the description does not address known sensitivities of attribution scores to local model initialization, architecture, and client-specific bias (especially in early FL rounds where the local model reflects only one distribution).
  2. [Experiments] Experimental results assert consistent superiority and faster convergence, but the abstract (and visible description) provides no dataset details, client counts, heterogeneity metrics (e.g., Dirichlet alpha), ablation studies on the attribution component, or error bars. Without these, the superiority claims cannot be evaluated.
minor comments (1)
  1. [Abstract] The abstract states 'theoretical privacy guarantees' but does not reference the specific metric privacy definition or proof sketch; if present in the full text, a forward reference would help.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the detailed and constructive report. We address each major comment below. Where the manuscript description is incomplete, we will revise accordingly while preserving the core claims supported by our experiments and analysis.

read point-by-point responses

  1. Referee: [Method] The method relies on the claim that a single backward-pass propagation attribution identifies precisely the data elements whose selective sharing reduces cross-client shift. This is load-bearing for the heterogeneity-mitigation claim, yet the description does not address known sensitivities of attribution scores to local model initialization, architecture, and client-specific bias (especially in early FL rounds where the local model reflects only one distribution).

    Authors: We agree that attribution scores can exhibit sensitivity to initialization and local bias. Our method applies propagation-based attribution (e.g., Layer-wise Relevance Propagation) on the aggregated global model after each communication round rather than purely local models, which reduces client-specific bias. We also note that single backward-pass methods are chosen for their relative stability compared to perturbation-based alternatives. We will add a dedicated paragraph in the method section discussing these sensitivities, citing relevant XAI literature, and include empirical checks (e.g., attribution consistency across initializations) in the supplementary material. revision: partial

  2. Referee: [Experiments] Experimental results assert consistent superiority and faster convergence, but the abstract (and visible description) provides no dataset details, client counts, heterogeneity metrics (e.g., Dirichlet alpha), ablation studies on the attribution component, or error bars. Without these, the superiority claims cannot be evaluated.

    Authors: The full manuscript contains these details: experiments use CIFAR-10 and Fashion-MNIST with 10–100 clients, Dirichlet alphas in {0.1, 0.5, 1.0}, multiple random seeds with error bars, and ablations isolating the attribution module. However, the abstract and early sections omit key parameters. We will revise the abstract to include dataset names, client range, heterogeneity parameterization, and mention of ablations and statistical reporting. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The manuscript describes FedXDS as a new method that applies propagation-based attribution in a single backward pass to select data for sharing in federated learning, augmented by metric privacy. No equations, parameter-fitting steps, or derivation chains appear in the abstract or described content. No self-citations are invoked as load-bearing uniqueness theorems, no fitted inputs are relabeled as predictions, and no ansatz or renaming of known results is presented as a first-principles derivation. The central claims rest on experimental comparisons and stated privacy guarantees rather than any self-referential reduction, rendering the approach self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no free parameters, axioms, or invented entities are described in sufficient detail to populate the ledger.

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discussion (0)

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