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REVIEW 2 major objections 2 minor 24 references

A sparse secure aggregation scheme enables top-K gradient use in decentralized federated learning while preserving accuracy and cutting communication.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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T0 review · grok-4.3

2026-06-27 11:39 UTC pith:G4GGWS7K

load-bearing objection The paper combines top-K sparsification with offline-phase info-theoretic secure aggregation in decentralized FL, but the abstract gives no protocol for aligning independent supports without reintroducing dimension-dependent costs or weakening security. the 2 major comments →

arxiv 2606.10780 v1 pith:G4GGWS7K submitted 2026-06-09 cs.IT cs.CRcs.LGmath.IT

Secure Aggregation with Top-K Sparsification in Decentralized Federated Learning

Hengxuan Tang , Jinbao Zhu , Xiaohu Tang This is my paper
classification cs.IT cs.CRcs.LGmath.IT
keywords secure aggregationtop-K sparsificationdecentralized federated learningcommunication efficiencygradient privacyinformation-theoretic securityuser dropouts
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper establishes a method for information-theoretically secure aggregation of top-K sparsified gradients in decentralized federated learning that handles user dropouts and collusion. It achieves this by protecting private gradients with random masks and permutations while moving dimension-dependent overhead to an offline phase. This addresses the problem that standard secure aggregation scales directly with gradient dimension, which becomes prohibitive for large models on bandwidth-limited decentralized networks. A sympathetic reader would care because the scheme reportedly maintains accuracy comparable to full-gradient aggregation even when only 1% of entries are transmitted.

Core claim

The authors introduce a communication-efficient sparse secure aggregation scheme for decentralized federated learning that supports top-K sparsification by allowing each user to select top-K indices independently, then applying random masks and permutations to protect the selected values, with all dimension-dependent costs handled offline.

What carries the argument

Random masking and permutation applied to independently chosen top-K indices, combined with offline precomputation to remove per-round dimension scaling.

Load-bearing premise

The top-K indices chosen independently by each user can be masked and permuted in an information-theoretically secure way without requiring per-round coordination that scales with model dimension.

What would settle it

An experiment in which the scheme either leaks information about which gradient entries were selected under collusion or produces accuracy noticeably below that of full-gradient aggregation when sparsity reaches 1%.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Communication cost per round no longer grows with the full model dimension.
  • Accuracy remains comparable to full-gradient aggregation at 1% sparsification.
  • The protocol stays secure against user dropouts and collusion in decentralized networks.
  • The method suits settings with limited bandwidth and unreliable nodes.

Where Pith is reading between the lines

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

  • The offline phase could be reused across multiple training rounds to further amortize setup costs.
  • Similar masking techniques might apply to other forms of gradient compression beyond top-K.
  • The approach could support larger models on edge devices where full secure aggregation was previously infeasible.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 2 minor

Summary. The paper proposes an information-theoretically secure aggregation protocol for decentralized federated learning that combines top-K gradient sparsification with random masks and permutations. Dimension-dependent work is offloaded to an offline phase; the scheme is claimed to handle user dropouts and collusion while achieving communication cost scaling with K rather than model dimension d. Experiments reportedly show that 1% sparsification preserves accuracy comparable to full-gradient aggregation and substantially reduces communication.

Significance. If the alignment of independently chosen top-K supports can be performed with information-theoretic security and O(K) per-round communication, the result would be significant for bandwidth-constrained decentralized FL deployments. The offline-phase design and the reported accuracy preservation at extreme sparsification are potentially valuable contributions; the experimental validation of end-to-end accuracy is a concrete strength.

major comments (2)
  1. [Proposed scheme description] Proposed scheme (abstract and §3): the description states that random masks and permutations are applied after offloading dimension-dependent work offline, yet provides no explicit mechanism for aligning user-specific top-K index sets so that masked values can be summed without either revealing indices, using a shared permutation (which would require d-scale coordination), or an online alignment step whose cost scales with d. This alignment step is load-bearing for both the claimed O(K) communication reduction and the information-theoretic security guarantee under independent per-user sparsification.
  2. [Security analysis] Security analysis (likely §4): the information-theoretic security claim under collusion and dropouts is not accompanied by a reduction or simulator argument that accounts for the permutation and masking steps after independent top-K selection; without this, it is unclear whether the offline phase truly eliminates online d-dependent leakage or coordination.
minor comments (2)
  1. [Abstract] The abstract and introduction would benefit from a concise statement of the exact per-round communication complexity (in bits or messages) achieved by the online phase.
  2. [Experiments] Experimental section should report the precise model dimension d, number of users, and dropout/collusion fractions used in the accuracy and communication measurements to allow direct comparison with the theoretical claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive feedback. We address the two major comments point by point below. Both comments correctly identify areas where the current manuscript description and analysis can be strengthened; we will revise accordingly.

read point-by-point responses

  1. Referee: [Proposed scheme description] Proposed scheme (abstract and §3): the description states that random masks and permutations are applied after offloading dimension-dependent work offline, yet provides no explicit mechanism for aligning user-specific top-K index sets so that masked values can be summed without either revealing indices, using a shared permutation (which would require d-scale coordination), or an online alignment step whose cost scales with d. This alignment step is load-bearing for both the claimed O(K) communication reduction and the information-theoretic security guarantee under independent per-user sparsification.

    Authors: We agree that the manuscript's description of the alignment mechanism for independently chosen top-K supports is insufficiently explicit. The scheme intends to achieve alignment via offline-generated random permutations applied to the full dimension (with only the resulting K masked values communicated online), but the current text does not spell out the precise steps that avoid both index revelation and d-scale online coordination. We will revise Section 3 to provide a detailed, step-by-step description of this process, including how the offline permutations enable secure summation of the sparse masked values while preserving the claimed O(K) online communication and information-theoretic security under independent sparsification. revision: yes

  2. Referee: [Security analysis] Security analysis (likely §4): the information-theoretic security claim under collusion and dropouts is not accompanied by a reduction or simulator argument that accounts for the permutation and masking steps after independent top-K selection; without this, it is unclear whether the offline phase truly eliminates online d-dependent leakage or coordination.

    Authors: We concur that the security analysis would benefit from an explicit simulator-based argument that incorporates the post-selection permutation and masking operations. The existing Section 4 analysis relies on the randomness of the masks and permutations to argue information-theoretic security against collusion and dropouts, but does not present a formal reduction. We will add a detailed security proof in the revised version that uses the simulation paradigm, explicitly modeling the top-K selection, offline permutations, and masks, and showing that the adversary's view is simulatable without knowledge of the private gradients. revision: yes

Circularity Check

0 steps flagged

No circularity: scheme proposal contains no self-referential derivations or fitted predictions.

full rationale

The paper proposes a sparse secure aggregation protocol for decentralized FL with top-K sparsification. The abstract and available description contain no equations, no fitted parameters, and no derivation chain that reduces a claimed result to its own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems, and no ansatz or renaming of known results is presented as a derivation. The central claims concern protocol construction and experimental accuracy preservation; these are independent of any internal circular reduction. This is the normal self-contained case for a protocol-design paper.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 1 invented entities

The scheme rests on standard cryptographic assumptions about random masks and permutations providing information-theoretic privacy; no free parameters or new entities are explicitly fitted or postulated in the abstract.

axioms (2)
  • domain assumption Random masks and permutations suffice for information-theoretic security of sparse gradients under user collusion
    Invoked in the description of the proposed scheme.
  • domain assumption An offline phase can be executed securely before training rounds begin
    Used to offload dimension-dependent overhead.
invented entities (1)
  • Sparse secure aggregation scheme with offline phase no independent evidence
    purpose: Enable efficient top-K secure aggregation in decentralized FL
    New protocol introduced to solve the stated challenge.

pith-pipeline@v0.9.1-grok · 5700 in / 1292 out tokens · 17624 ms · 2026-06-27T11:39:38.203968+00:00 · methodology

0 comments
read the original abstract

Secure aggregation is a vital component for mitigating gradient leakage in federated learning, but its communication cost conventionally scales with the gradient dimension. This becomes prohibitive for large models and even more pronounced in decentralized federated learning with limited bandwidth and unreliable nodes. Top-K gradient sparsification is an effective approach to reduce communication by transmitting only a few entries of the full gradient, while maintaining competitive model accuracy. Nevertheless, the top-K entries selected by each user are unpredictable and vary across users, which poses a challenge for efficient sparse secure aggregation. This paper studies information-theoretic secure aggregation with top-K sparsification in decentralized federated learning under user dropouts and user collusion. We propose a communication-efficient sparse secure aggregation scheme that offloads dimension-dependent overhead to an offline phase and protects private gradients using random masks and permutations. Experimental results demonstrate that our scheme preserves accuracy comparable to full-gradient aggregation even with only 1% gradient sparsification, while substantially reducing the communication cost.

Figures

Figures reproduced from arXiv: 2606.10780 by Hengxuan Tang, Jinbao Zhu, Xiaohu Tang.

Figure 1
Figure 1. Figure 1: Test accuracy under varying user dropout rates. [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗

discussion (0)

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Reference graph

Works this paper leans on

24 extracted references · 4 canonical work pages

  1. [1]

    Communication-efficient learning of deep networks from decentralized data,

    B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-efficient learning of deep networks from decentralized data,” inArtificial intelligence and statistics. PMLR, 2017, pp. 1273– 1282

    2017

  2. [2]

    Deep leakage from gradients,

    L. Zhu, Z. Liu, and S. Han, “Deep leakage from gradients,”Advances in neural information processing systems, vol. 32, 2019

    2019

  3. [3]

    iDLG: Improved Deep Leakage from Gradients

    B. Zhao, K. R. Mopuri, and H. Bilen, “idlg: Improved deep leakage from gradients,”arXiv preprint arXiv:2001.02610, 2020

    work page Pith review arXiv 2001

  4. [4]

    Practical secure aggregation for privacy-preserving machine learning,

    K. Bonawitz, V . Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical secure aggregation for privacy-preserving machine learning,” inproceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, 2017, pp. 1175–1191

    2017

  5. [5]

    Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,

    J. So, B. Güler, and A. S. Avestimehr, “Turbo-aggregate: Breaking the quadratic aggregation barrier in secure federated learning,”IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 1, pp. 479–489, 2021

    2021

  6. [6]

    Swiftagg+: Achieving asymptotically optimal communication loads in secure ag- gregation for federated learning,

    T. Jahani-Nezhad, M. A. Maddah-Ali, S. Li, and G. Caire, “Swiftagg+: Achieving asymptotically optimal communication loads in secure ag- gregation for federated learning,”IEEE Journal on Selected Areas in Communications, vol. 41, no. 4, pp. 977–989, 2023

    2023

  7. [7]

    Information theoretic secure aggregation with user dropouts,

    Y . Zhao and H. Sun, “Information theoretic secure aggregation with user dropouts,”IEEE Transactions on Information Theory, vol. 68, no. 11, pp. 7471–7484, 2022

    2022

  8. [8]

    Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,

    J. So, C. He, C.-S. Yang, S. Li, Q. Yu, R. E Ali, B. Guler, and S. Avestimehr, “Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,”Proceedings of Machine Learning and Systems, vol. 4, pp. 694–720, 2022

    2022

  9. [9]

    On secure aggregation with uncoded groupwise keys against user dropouts and user collusion,

    Z. Zhang, J. Liu, K. Wan, H. Sun, M. Ji, and G. Caire, “On secure aggregation with uncoded groupwise keys against user dropouts and user collusion,”IEEE Transactions on Information Theory, vol. 71, no. 11, pp. 8391–8413, 2025

    2025

  10. [10]

    Fully decentralized federated learning,

    A. Lalitha, S. Shekhar, T. Javidi, and F. Koushanfar, “Fully decentralized federated learning,” inThird workshop on bayesian deep learning (NeurIPS), vol. 12, 2018

    2018

  11. [11]

    Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,

    E. T. M. Beltrán, M. Q. Pérez, P. M. S. Sánchez, S. L. Bernal, G. Bovet, M. G. Pérez, G. M. Pérez, and A. H. Celdrán, “Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges,”IEEE Communications Surveys & Tutorials, vol. 25, no. 4, pp. 2983–3013, 2023

    2023

  12. [12]

    Decentralized federated learning: A survey and perspective,

    L. Yuan, Z. Wang, L. Sun, P. S. Yu, and C. G. Brinton, “Decentralized federated learning: A survey and perspective,”IEEE Internet of Things Journal, vol. 11, no. 21, pp. 34 617–34 638, 2024

    2024

  13. [13]

    Information- theoretic decentralized secure aggregation with collusion resilience,

    X. Zhang, Z. Li, S. Li, K. Wan, D. W. K. Ng, and G. Caire, “Information- theoretic decentralized secure aggregation with collusion resilience,” arXiv preprint arXiv:2508.00596, 2025

    work page arXiv 2025

  14. [14]

    The capacity of collusion-resilient decentralized secure aggregation with groupwise keys,

    Z. Li, X. Zhang, Y . Zhao, H. Chen, J. Fan, and G. Caire, “The capacity of collusion-resilient decentralized secure aggregation with groupwise keys,”arXiv preprint arXiv:2511.14444, 2025

    work page arXiv 2025

  15. [15]

    Optimal key rates for decentralized secure aggregation with arbitrary collusion and heterogeneous security constraints,

    Z. Li, X. Zhang, and G. Caire, “Optimal key rates for decentralized secure aggregation with arbitrary collusion and heterogeneous security constraints,”arXiv preprint arXiv:2512.16112, 2025

    work page arXiv 2025

  16. [16]

    Sparse communication for distributed gradient descent,

    A. F. Aji and K. Heafield, “Sparse communication for distributed gradient descent,” inProceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, 2017, pp. 440–445

    2017

  17. [17]

    Deep Gradient Compression: Reducing the communication bandwidth for distributed training,

    Y . Lin, S. Han, H. Mao, Y . Wang, and W. J. Dally, “Deep Gradient Compression: Reducing the communication bandwidth for distributed training,” inThe International Conference on Learning Representations, 2018

    2018

  18. [18]

    Grace: A compressed communication framework for distributed machine learning,

    H. Xu, C.-Y . Ho, A. M. Abdelmoniem, A. Dutta, E. H. Bergou, K. Karatsenidis, M. Canini, and P. Kalnis, “Grace: A compressed communication framework for distributed machine learning,” in2021 IEEE 41st international conference on distributed computing systems (ICDCS). IEEE, 2021, pp. 561–572

    2021

  19. [19]

    Secure gradient aggregation with sparsifi- cation for resource-limited federated learning,

    H. U. Sami and B. Güler, “Secure gradient aggregation with sparsifi- cation for resource-limited federated learning,”IEEE Transactions on Communications, vol. 72, no. 11, pp. 6883–6899, 2024

    2024

  20. [20]

    Lagrange coded computing: Optimal design for resiliency, security, and privacy,

    Q. Yu, S. Li, N. Raviv, S. M. M. Kalan, M. Soltanolkotabi, and S. A. Avestimehr, “Lagrange coded computing: Optimal design for resiliency, security, and privacy,” inThe 22nd International Conference on Artificial Intelligence and Statistics. PMLR, 2019, pp. 1215–1225

    2019

  21. [21]

    Symmetric private polynomial computation from lagrange encoding,

    J. Zhu, Q. Yan, X. Tang, and S. Li, “Symmetric private polynomial computation from lagrange encoding,”IEEE Transactions on Informa- tion Theory, vol. 68, no. 4, pp. 2704–2718, 2022

    2022

  22. [22]

    Generalized lagrange coded computing: A flexible computation-communication tradeoff,

    J. Zhu and S. Li, “Generalized lagrange coded computing: A flexible computation-communication tradeoff,” in2022 IEEE International Sym- posium on Information Theory (ISIT). IEEE, 2022, pp. 832–837

    2022

  23. [23]

    Secure single-server aggregation with (poly) logarithmic overhead,

    J. H. Bell, K. A. Bonawitz, A. Gascón, T. Lepoint, and M. Raykova, “Secure single-server aggregation with (poly) logarithmic overhead,” in Proceedings of the 2020 ACM SIGSAC conference on computer and communications security, 2020, pp. 1253–1269

    2020

  24. [24]

    Fastsecagg: Scalable secure aggregation for privacy-preserving feder- ated learning,

    S. Kadhe, N. Rajaraman, O. O. Koyluoglu, and K. Ramchandran, “Fastsecagg: Scalable secure aggregation for privacy-preserving feder- ated learning,” in2020 CCS Workshop on Privacy-Preserving Machine Learning in Practice, Orlando, FL, Nov. 2020

    2020