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REVIEW 5 major objections 4 minor 16 references

Verifiable Unlearning on Edge

T0 review · 5 major / 4 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper argues that pruning plus a second-order weight repair, proven with a zero-knowledge proof, makes machine unlearning on personalized edge models verifiable and private.

desk verdict A promising combination of pruning, OBS, and a zk-SNARK for edge unlearning, but the proof only certifies arithmetic on commitments, so the central 'verifiable' claim is unsupported and the evaluation is too thin to save it. read the letter →

arxiv 2506.20037 v1 pith:F5NQL5RC submitted 2025-06-24 cs.LG cs.CR

Mohammad M Maheri , Alex Davidson , Hamed Haddadi This is my paper · ORCID
classification cs.LGcs.CR
keywords machineunlearningzero-knowledgeproofszk-SNARKedgecomputingneuralnetworkpruningOptimalBrainSurgeonmodelpersonalizationprivacy-preservingverification
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

This paper proposes a way to make machine unlearning on personalized edge devices verifiable without exposing private data. Instead of retraining, the provider sends a pruning mask that targets neurons most strongly activated by the data to be forgotten; the client applies the mask to its locally fine-tuned model and then repairs the damage using an Optimal Brain Surgeon weight adjustment (a second-order correction of the surviving weights) computed from a private, block-diagonal curvature estimate. A zk-SNARK (a compact cryptographic proof that a computation was done correctly without revealing its inputs) lets the client prove to the provider that this update was computed from the committed inputs and that the new model commitment is the old one plus the update, while the weights and data stay hidden. The paper reports that on a Vision Transformer fine-tuned on a sketch-domain dataset, the forget-class accuracy drops from 93.7% to 59.5%, while personalized accuracy stays near its baseline, showing the correction recovers most of the utility lost by naive pruning.

What carries the argument

The carrying object is Algorithm 1, a client-side procedure that commits to a block-diagonal empirical Fisher matrix $H^P_L$, receives a pruning mask, computes Optimal Brain Surgeon updates, and emits a zk-SNARK proof. The mask encodes which MLP parameters to delete, chosen by the importance-score ratio; the Fisher matrix is committed before any unlearning request and represents the personalized loss landscape locally. The proof certifies (i) that each weight adjustment $\delta w^*$ was computed from $H^P_L$ and the mask according to the OBS update, and (ii) that the new model commitment is the old commitment plus the sparse update. The block-diagonal structure is what makes the proof cheap: it turns the computation into independent per-block verification of linear operations, avoiding the nonlinearities that make training-stage proofs prohibitively expensive.

What would settle it

Run Algorithm 1 across several personalization seeds, recompute the importance scores using each client's own fine-tuned weights, and compare the provider's mask to the client-local forget neurons; if the overlap is low and forget-class accuracy remains high after the update, the mask-transfer premise fails. In the same setup, replace the block-diagonal Fisher matrix with the full Hessian and check whether utility recovery changes, which would show whether the curvature approximation is the load-bearing part.

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Extended reading notes

Core claim

The central claim is that approximate unlearning can be split into a pruning step and a second-order repair step, and that both steps can be made cheap enough to prove in zero knowledge. For each neuron the provider computes an importance score $\mathrm{Score}(n;D_{\mathrm{retain}},D_{\mathrm{forget}})=\mathrm{Importance}(D_{\mathrm{forget}},n)/(\mathrm{Importance}(D_{\mathrm{retain}},n)+\epsilon)$, prunes the highest-scoring neurons, and sends the mask to the client. The client then adjusts the remaining weights using the Optimal Brain Surgeon rule $\delta w^* = -\frac{w_i}{[H_L(w^*)^{-1}]_{ii}} H_L(w^*)^{-1} e_i$, where the Hessian is replaced by a block-diagonal empirical Fisher matrix computed on the personalization dataset. Because the curvature matrix is block-diagonal, the update decomposes into independent matrix-vector products per block, which is exactly the kind of structured arithmetic a zk-SNARK can verify efficiently; the client proves the update was computed from the committed Fisher matrix and mask, and that the new model commitment is the old commitment plus the update, without revealing parameters or data. In the paper's experiment this recovers most of the personalized accuracy lost to naive pruning while further lowering accuracy on the forget class.

Load-bearing premise

The load-bearing premise is that a pruning mask picked by the provider from its global model still marks the neurons that actually carry the forget class in each user's fine-tuned copy, and that a block-diagonal Fisher approximation of the personalized loss is accurate enough for the weight repair; the paper measures neither.

Editorial extensions

If this is right

  • A compliant edge client can prove to a model provider that a deletion request was processed without uploading its personalized weights, the forget set, or its curvature matrix.
  • Because the proof decomposes over blocks and uses only sparse linear operations, proof generation avoids the orders-of-magnitude cost of proving full retraining, keeping unlearning verification plausible on constrained devices.
  • The OBS repair step does double duty: it lowers forget-class accuracy further and recovers most of the personalized accuracy lost by applying the mask alone, so unlearning does not erase personalization value.
  • The framework's unlearning guarantee is approximate and tied to the pruning mask: the targeted data's influence is sharply reduced rather than provably removed in the exact-retraining sense.
  • The same mask-plus-commitment design can in principle be applied to any model with a pruning criterion and a block-diagonal curvature approximation, including the large language models named as future work.

Reading between the lines

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

  • Editorial inference: the provider's mask is chosen once from the global pretrained model, while personalization shifts each client's weights; the scheme's effectiveness depends on whether those same neurons still carry the forget set after fine-tuning, something the paper does not measure.
  • Editorial inference: the block-diagonal Fisher commitment implicitly assumes the off-block curvature is negligible for the OBS correction; on models with heavily entangled features that assumption may break and utility recovery could be smaller than reported.
  • Editorial inference: the paper positions the method as edge-friendly but reports no proof-generation time or memory measurements; a concrete benchmark against proof-of-retraining baselines would be the decisive test of that claim.
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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 / 4 minor

Summary. The paper proposes a framework for verifiable machine unlearning on edge-device personalized models. The unlearning method combines neuron pruning based on activation importance over a forget set with Optimal Brain Surgeon (OBS) weight adjustment using a block-diagonal empirical Fisher matrix. The verification component is a zk-SNARK that is meant to let a client prove that the model update was performed according to Equation (1) and that the new model commitment equals the old commitment plus the update, without revealing model parameters or personalization data. The evaluation is a single experiment on a ViT model fine-tuned on ImageNet-Sketch, measuring forget-class accuracy and personalized accuracy before and after pruning and after OBS adjustment. The paper claims that the methodology ensures verifiable, privacy-preserving, and effective machine unlearning across edge devices.

Significance. If the protocol worked as stated, it would address a real and timely problem: proving compliance with deletion requests on locally personalized models without leaking private data. The proposed use of block-diagonal Fisher information and sparse updates to keep the proof statement zk-friendly is a plausible design direction. However, the paper's contribution is currently a position proposal rather than a demonstrated system. There are no machine-checked proofs, no reproducible code, no proof-generation measurements, and the only experimental evidence is a single unvalidated number. The verification statement as specified does not logically bind the committed values to real data or to the forget set, so the central promise of 'verifiable unlearning' is not met even in principle. The paper may be useful as a starting point for discussion, but it does not yet establish its central claims.

major comments (5)
  1. [II-B, Algorithm 1] Algorithm 1's proof statement (lines 13-14) only requires the prover to demonstrate that δw* was computed from the committed Fisher matrix comH and the mask via Equation (1), and that com'_P = comP + δw*. These are purely algebraic relations among commitments; nothing in the proof binds comH to the client's personalized dataset, comP to the actually deployed model, or the mask to the forget set. A dishonest client can therefore commit to an arbitrary block-diagonal matrix and an arbitrary current model, then generate a valid proof for a sparse update that has no connection to the requested unlearning. The stated protocol therefore does not provide verifiable unlearning, even with a perfect zk-SNARK implementation.
  2. [III-A, Table I] The pruning mask is computed using Score(n; Dforget, Dretain) over the forget set, and the unlearning outcome is measured as accuracy on the same forget class ("birds") of the same data used to derive the mask. This makes the observed drop from 93.7% to 60.5% partly circular: the mask is deliberately constructed to suppress activations on Dforget, so measuring forgetting on Dforget is expected to show a decrease by construction. The paper provides no held-out forget set or independent measure of data influence removal.
  3. [III] The experimental evaluation consists of a single run reported in Table I, with no error bars, no multiple clients or seeds, and no comparison to standard unlearning baselines such as retraining from scratch, gradient ascent, or certificate-based methods. The central quantitative claim of effective unlearning with minimal utility loss rests entirely on this one unvalidated table, which is insufficient support for the abstract's assertion of practicality and effectiveness.
  4. [II-A] The methodology assumes that a pruning mask computed on the global pretrained model remains valid for each client's personally fine-tuned model. The paper does not measure the transfer of the mask from the global model to the personalized models, nor does it assess whether the block-diagonal empirical Fisher matrix computed on the personalized dataset accurately approximates the curvature needed for OBS adjustment. Without such measurements, the claim that OBS adjustment preserves personalization while forgetting the target class is not empirically grounded.
  5. [III, IV] The zk-SNARK component is entirely unimplemented: the paper explicitly defers "a concrete evaluation of proof generation costs" to future work. For a paper whose title and abstract promise verifiable unlearning, the absence of any proof-generation measurements, proof size, or memory footprint means the practical feasibility claim is unsupported by data.
minor comments (4)
  1. [I] The phrase "ensuring minimal computational and memory overhead" is stated as a result in the abstract, but no overhead measurements are reported anywhere in the paper.
  2. [Figure 1] Figure 1 is described as a framework overview but contains almost no detail; the relationship between the client's phishing step, the provider's mask computation, and the zk-SNARK verification is unclear from the figure alone.
  3. [Table I] The row "Improvement over Naive Mask (%) 1.7 71.4" is unclear: it appears to mix percentage-point improvements with relative improvements, and the column headers do not make explicit which metric each number refers to.
  4. [Throughout] There are formatting artifacts such as the arXiv identifier and submission date embedded in the text, and the equation for the importance score is not numbered, making it awkward to reference.

Circularity Check

1 steps flagged · score 6.0 of 10

Verifier's proof statement is commitment arithmetic, so 'verifiable unlearning' reduces by definition; pruning/Fisher evaluations are in-sample but not circular.

  1. self definitional [Section II-B, Algorithm 1 (lines 12-15)]
    "Generate zkSNARK proof π that: (i) δw∗ computed correctly from comH and mask (ii) com′P = comP + δw∗ ... Verifier checks π and updated commitment com′P"

    The verifier's acceptance condition is defined by these two arithmetic relations on commitments. The abstract's claim that the framework 'confirm[s] data unlearning' and 'ensures verifiable, privacy-preserving, and effective machine unlearning' therefore contains, by construction, only those relations: the proof never checks that comH was computed from the personalized dataset, that comP commits to the deployed model, or that the mask derives from the forget set. A dishonest client can commit to arbitrary values and produce a valid proof for any sparse update.

full rationale

The unlearning algorithm itself is evaluated on external benchmarks (ImageNet-pretrained ViT, ImageNet-Sketch personalization), and the OBS update is a standard formula, so the empirical unlearning claims are not circular: the pruning mask is selected from activation magnitudes, not from accuracy, and the Fisher-based adjustment is not guaranteed to improve the reported metrics. The self-citation [1] (Maheri et al., PETS 2025) is used only to motivate zk-SNARK DNN verification and is not load-bearing. The central 'verifiable' claim, however, is circular in a different sense: Algorithm 1 defines the verifier's check purely as two commitment-arithmetic relations, and the abstract calls this 'confirm[ing] data unlearning' and 'ensur[ing] verifiable ... machine unlearning.' Because the proof statement never binds comH or comP to real data or checks the mask's provenance, the verification outcome is by construction equivalent to 'the update matches the committed values,' not to 'the requested data was removed.' This is a definitional reduction of the paper's headline property, so the circularity score is 6. The lack of a zk-SNARK implementation and proof-cost measurements is a missing-support issue, not circularity.

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

The central protocol assumes specific curvature and mask-transfer properties without measurement, and the verification claim adds a cryptographic efficiency assumption that is explicitly deferred. The 2% pruning ratio and epsilon are hand-chosen. No new entities are postulated.

free parameters (3)
  • pruning_ratio = 2% of MLP parameters
    Chosen by hand in Section III; directly controls the strength of forgetting and utility loss, with no sensitivity analysis.
  • epsilon in neuron importance score = not specified
    Added to the denominator of Score(n; Dforget, Dretain) to avoid division by zero; its value affects which neurons are pruned.
  • Fisher block-diagonal structure = not specified
    The block size and diagonal approximation level are not reported; these choices determine both proof complexity and the quality of OBS adjustment.
assumptions (4)
  • domain assumption Neuron activation magnitude over a dataset reflects that neuron's influence on predictions for that dataset.
    This is the basis for selecting neurons to prune (Section II-A), but no evidence connects activation magnitude to actual forget-set influence.
  • domain assumption The second-order Taylor expansion of the loss with the empirical Fisher matrix approximates the true Hessian well enough for OBS adjustment.
    Standard OBS relies on Hessian accuracy (Equation (1)); the paper substitutes empirical Fisher on a small personalization set without validating the approximation.
  • ad hoc to paper A mask computed on the global pretrained model transfers to personalized edge models.
    The provider computes the mask from the global model, but clients have fine-tuned personalized models; the paper itself notes that indiscriminate pruning hurts personalization and offers no transfer test.
  • domain assumption zk-SNARKs can efficiently verify block-diagonal matrix-vector multiplications and sparse model updates inside a commitment scheme.
    This is the foundation of Algorithm 1's practicality, but no proof-generation experiment is reported; the paper lists this as future work.

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Cite this review

Pith. "Pith review of Verifiable Unlearning on Edge." pith.science (2026). https://pith.science/paper/F5NQL5RC

@misc{pith2026250620037,
  author       = {Pith},
  title        = {Pith review of: Verifiable Unlearning on Edge},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F5NQL5RC}},
  note         = {Machine review of arXiv:2506.20037}
}
read the original abstract

Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edge devices. Ensuring that edge devices correctly execute such unlearning operations is critical to maintaining integrity. In this work, we introduce a verification framework leveraging zero-knowledge proofs, specifically zk-SNARKs, to confirm data unlearning on personalized edge-device models without compromising privacy. We have developed algorithms explicitly designed to facilitate unlearning operations that are compatible with efficient zk-SNARK proof generation, ensuring minimal computational and memory overhead suitable for constrained edge environments. Furthermore, our approach carefully preserves personalized enhancements on edge devices, maintaining model performance post-unlearning. Our results affirm the practicality and effectiveness of this verification framework, demonstrating verifiable unlearning with minimal degradation in personalization-induced performance improvements. Our methodology ensures verifiable, privacy-preserving, and effective machine unlearning across edge devices.

Figures

Figures reproduced from arXiv: 2506.20037 by the authors.

Figure 1
Figure 1. Framework overview of the proposed privacy-preserving method. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

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Reference graph

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