REVIEW 3 major objections 6 minor 41 references
Byzantine-robust aggregation rules such as multi-Krum can be replicated without a coordinator or consensus, yielding byte-identical aggregates once honest replicas share the same updates.
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 →
T0 review · grok-4.5
2026-07-14 12:45 UTC pith:RYBZDF6S
load-bearing objection Consistency claim is elementary and solid; the real contribution is the composition that makes discontinuous multi-Krum byte-identical without consensus, with robustness correctly left conditional. the 3 major comments →
Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
Core claim
Any pure total function of a product of convergent replicated data types inherits Strong Eventual Consistency, even when the function is non-monotone, non-associative, or stochastic with state-derived entropy. Applied to multi-Krum, honest replicas that have received the same contributions and the same offline-verifiable equivocation proofs therefore compute byte-identical aggregates without agreeing on arrival order or running a consensus protocol.
What carries the argument
The product-lifting theorem (any pure function of a converged product of CvRDTs inherits SEC) applied to the ACFA product state: a contribution OR-Set composed with a grow-only set of self-authenticating equivocation proofs, reduced by a fixed-point, hash-ordered multi-Krum kernel that is a pure function of the admitted set.
Load-bearing premise
The imported statistical robustness of multi-Krum only holds when the honest score gap is large enough to survive the worst-case rounding error of fixed-point arithmetic; the paper does not prove this always occurs.
What would settle it
Re-run or re-implement the 16-check falsification battery under adversarial schedules: if any two honest replicas that have received the same updates ever produce different 32-byte output roots, or if a late equivocation proof fails to re-converge them to one root, the consistency claim is false.
If this is right
- Coordinator-free federated or gossip learning can force exact, not merely statistical, agreement on the robust aggregate.
- A late-arriving proof of equivocation deterministically re-converges every honest replica to the same post-eviction output with no extra protocol.
- The same lifting applies unchanged to any other pure robust selector (for example Bulyan), so the consistency layer is rule-agnostic.
- Accountability becomes permanent eviction of the offending key rather than per-round statistical re-filtering.
- Tolerance-based approximate agreement is unnecessary for discontinuous selectors once exact integer arithmetic is used.
Where Pith is reading between the lines
- The same product-of-lattices pattern could gate other discontinuous reductions that currently sit behind coordinators, such as median-of-means or clustering-based filters.
- Multi-round training still needs an external quiescence or epoch-closure signal; the per-round safety guarantee alone does not prevent temporary forks of the round-DAG.
- A closed-form anti-concentration bound on score gaps would turn the quantisation-margin caveat from an empirical check into a high-probability guarantee.
- Permissionless deployments would need a Sybil-resistant identity layer in place of the assumed PKI before the construction can leave consortia.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper presents ACFA, a coordinator-free construction that replicates Byzantine-robust aggregation (full-dimension multi-Krum, optionally Bulyan) by composing a content-addressed contribution OR-Set with a grow-only set of self-authenticating equivocation proofs, then evaluating the selector as a pure total function of the converged product state under Q16.16 fixed-point arithmetic and hash-canonical order. It proves that any pure function of a product of CvRDTs inherits Strong Eventual Consistency (Theorem 7), gives a converse characterisation of realisable implementations (Theorem 8), and shows accountable permanent eviction by offline-verifiable proofs without votes (Theorem 10). Consistency (Theorem 14 Part A) is separated from imported, conditional robustness (Part B) and from multi-round quiescence. A prototype (n=10, f≤3) passes a 16-check falsification battery including three ablations that each break byte-identity, plus late-proof re-convergence and partition healing.
Significance. If the results hold, the paper cleanly shows that global coupling, non-associativity, and discontinuity of robust selectors do not force a coordinator or consensus for safety: only an agreed set and an agreed exclusion predicate are required, both of which are join-semilattices. The product-lifting theorem is elementary (as the authors state), but the composition of a data lattice with an evidence lattice gating a discontinuous selector under exact integer arithmetic, together with the converse (Theorem 8) and the late-proof re-convergence property (Theorem 10(iii)), is a useful and carefully scoped contribution for Byzantine-CRDT and decentralised-learning systems. Strengths include honest separation of consistency from accuracy and of safety from liveness, discharge of environmental determinism by integer arithmetic forced by Lemma 2, a falsification battery with mechanism-removing ablations, and explicit open items (quantisation-margin anti-concentration, multi-round epoch sync). The robustness leg is imported and conditional, which the paper correctly labels rather than overclaiming.
major comments (3)
- Theorem 14 Part B and Lemma 12 / Remark 13: Part B states that the imported multi-Krum envelope holds under |A|≥2f+3 and the quantisation-margin condition g>2β, while Remark 13 leaves a closed-form anti-concentration bound on the score-gap distribution as open and reports only empirical zero flips on 1600 configurations (including near-tie). Because multi-Krum is discontinuous (Lemma 2), a residual flip moves the aggregate by Δ/m. Please rephrase Part B so it is unambiguously a conditional corollary of [5] under a checkable margin predicate, not a free-standing robustness theorem of this paper; either supply a high-probability bound under a stated honest-input model or demote the envelope language further so that the only unconditional claim remains Part A.
- Section 1 (scope) and Section 7 (multi-round execution): the consensus-free claim is correctly restricted to per-round resolve over a converged admitted set, with round-closure / epoch forking delegated to an orthogonal mechanism. This is load-bearing for any multi-round training pipeline that would actually use ACFA. Elevate this limitation into the abstract and the contributions list with the same prominence as “consistency, not accuracy,” so that the title claim cannot be over-read as end-to-end multi-round coordinator-free training.
- Reproducibility (final paragraph and Appendix A): the prototype and harnesses are stated to be released upon publication and available on request. For a systems paper whose central empirical support is a 16/16 binary falsification battery plus three ablations, the artifact should be available to reviewers (or a permanent anonymous archive linked in the submission). Without it, the byte-identity claims and the Lemma 1 / A3 transcripts cannot be independently checked at review time.
minor comments (6)
- Section 5, Theorem 8: the measure-theoretic entropy argument is sketched rather than fully formalised (“measurability details are in the proof”). For journal archival, expand the Borel-set argument or move a complete proof to an appendix so the converse is checkable without reconstruction.
- Section 6.1 / Table 1: state explicitly that all checks are single-architecture; cross-architecture byte-identity is argued from integer semantics (Section 7.1) but not measured. A one-sentence caveat in the table caption would prevent over-reading.
- Remark 5 and Section 4.3: the retirement of the subsampled / Merkle-seeded kernel is well motivated (grinding, bound mismatch). Consider moving the grinding negative result (X9, 0/900) into the main text near Remark 5 so the design change is evidenced where it is introduced.
- Related Work (Section 8): the distinctions from BRACE, IOS/WFAgg/BALANCE, blocklace, and Brocco are useful. A short comparison table (guarantee class: statistical convergence vs byte-identity; object protected: state vs discontinuous reduction; evidence actuation: graded trust vs binary conviction) would help readers place the contribution faster.
- Notation: Admit / Visible / Convicted / R are defined cleanly, but the product merge is written both as ⊔1×⊔2 and as componentwise union. Pick one notation and use it consistently in Theorems 6–7.
- Typos / polish: “ulpscale” → “ulp-scale”; abstract “2f + 3” vs body “≥2f+3” — align; a few long sentences in Section 3.2 and Remark 13 would benefit from splitting for readability.
Circularity Check
No significant circularity: product-lifting SEC is elementary from purity + CvRDT convergence; self-citations are scaffolding, not load-bearing for the central claim.
full rationale
The paper’s strongest consistency claim (Theorem 7 / Theorem 14 Part A) is that any pure total function of a converged product of CvRDTs evaluates identically at replicas that have received the same updates. The proof is self-contained and non-circular: product of CvRDTs is a CvRDT (Theorem 6, componentwise join), CvRDT convergence yields equal states [37], and a pure function of equal arguments yields equal values. Stochastic selectors are admitted only when entropy is state-derived; the converse (Theorem 8) characterises rather than assumes that restriction. The paper itself labels the lifting step elementary and locates its contribution in the composition (contribution OR-Set × equivocation G-Set + discontinuous multi-Krum under fixed-point arithmetic), not in a novel algebra. Self-citations to the author’s prior two-layer architecture [19] and E4 [20] supply scaffolding and an explicit reduction-class contrast (smooth trust-weighted mean vs discontinuous selector); they are not invoked as uniqueness theorems that force the present result, and the proofs of Theorems 6–8 and 10 do not depend on those works being true. Robustness bounds are imported from Blanchard et al. [5] and Mhamdi et al. [32] under stated population and quantisation-margin conditions that the paper leaves partly open (Remark 13)—conditional import, not circular self-prediction. Experimental checks are binary falsifications of byte-identity, not fitted parameters re-labelled as predictions. Score 1 reflects only routine self-citation for architectural lineage, not a reduction of the central claim to its inputs.
Axiom & Free-Parameter Ledger
free parameters (2)
- Q16.16 quantisation step δ = 2^{-16}
- multi-Krum selection count m = |A| - f - 2
axioms (5)
- standard math CvRDT convergence theorem: replicas that have received the same set of updates converge to identical states regardless of order, duplication or delay.
- domain assumption PKI: every contribution and proof is signed under a public-key infrastructure that authenticates identities.
- domain assumption Eventual pairwise delivery among honest replicas.
- domain assumption Imported multi-Krum / Bulyan statistical robustness under |A| ≥ 2f+3 (resp. 4f+3) and at most f Byzantine admitted contributions, plus the quantisation-margin condition g > 2β of Lemma 12.
- domain assumption Integer (fixed-point) arithmetic is exact and identical on any architecture implementing the stated width contract.
invented entities (2)
-
ACFA product state (contribution OR-Set × equivocation-proof G-Set)
independent evidence
-
Admit / Resolve pure functions (Definition 3–4)
independent evidence
Cite this review
Pith. "Pith review of Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation." pith.science (2026). https://pith.science/paper/RYBZDF6S
@misc{pith2026260710305,
author = {Pith},
title = {Pith review of: Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation},
year = {2026},
howpublished = {\url{https://pith.science/paper/RYBZDF6S}},
note = {Machine review of arXiv:2607.10305}
}
read the original abstract
Byzantine-robust aggregation rules such as multi-Krum assume a central coordinator, and decentralising them is obstructed by the rules themselves: they are globally coupled, non-associative, and discontinuous, so an ulpscale perturbation can flip the selected subset, moving the output by a non-vanishing amount. None of this prevents coordinator-free replication, because a robust rule needs no agreed order of contributions, only an agreed set and an agreed exclusion predicate, both of which converge without consensus. ACFA (Accountable Consensus-Free Aggregation) replicates a content-addressed OR-Set of signed contributions and a grow-only set of self-authenticating equivocation proofs, offline-verifiable by anyone. Aggregation is a deterministic pure function of the converged product state: fixed-point integer arithmetic over a hash-canonical order, ties broken by content hash. We prove that any pure function of a converged product of CRDTs (non-monotone, non-associative, or stochastic) inherits Strong Eventual Consistency, together with its converse; the contribution is the composition of a data lattice with an evidence lattice applied to a robust selector, not the elementary lifting step. A prototype (10 nodes, 3 Byzantine) passes 16/16 falsification checks: byte-identical roots under adversarial gossip, deterministic re-convergence after late equivocation proofs, partition recovery, and three byte-identity-breaking ablations. The guarantee is consistency, not accuracy; robustness is imported, conditional on 2f + 3 admitted contributions (at most f Byzantine) and a stated quantisation-margin condition.
Reference graph
Works this paper leans on
-
[1]
Paulo Sérgio Almeida and Ehud Shapiro. The blocklace: A Byzantine-repelling and universal conflict-free replicated data type.arXiv preprint arXiv:2402.08068, 2024
Pith/arXiv arXiv 2024
-
[2]
Delta state replicated data types.Journal of Parallel and Distributed Computing, 111:162–173, 2018
Paulo Sérgio Almeida, Ali Shoker, and Carlos Baquero. Delta state replicated data types.Journal of Parallel and Distributed Computing, 111:162–173, 2018
2018
-
[3]
Composition in state-based replicated data types.Bulletin of the European Association for Theoretical Computer Science (EATCS), 123, 2017
Carlos Baquero, Paulo Sérgio Almeida, Alcino Cunha, and Carla Ferreira. Composition in state-based replicated data types.Bulletin of the European Association for Theoretical Computer Science (EATCS), 123, 2017
2017
-
[4]
A little is enough: Circumventing defenses for distributed learning
Gilad Baruch, Moran Baruch, and Yoav Goldberg. A little is enough: Circumventing defenses for distributed learning. InAdvances in Neural Information Processing Systems 32 (NeurIPS), 2019
2019
-
[5]
Machine learning with adversaries: Byzantine tolerant gradient descent
Peva Blanchard, El Mahdi El Mhamdi, Rachid Guerraoui, and Julien Stainer. Machine learning with adversaries: Byzantine tolerant gradient descent. InAdvances in Neural Information Processing Systems 30 (NeurIPS), pages 119–129, 2017
2017
-
[6]
Amos Brocco. A composable CRDT layer for Byzantine-resilient deterministic reconstruction.arXiv preprint arXiv:2606.18966, 2026
Pith/arXiv arXiv 2026
-
[7]
Amos Brocco. Decoupling trust in Byzantine CRDTs: Fine-grained post-compromise handling without breaking causality.arXiv preprint arXiv:2606.31759, 2026
Pith/arXiv arXiv 2026
-
[8]
Casper the friendly finality gadget.arXiv preprint arXiv:1710.09437, 2017
Vitalik Buterin and Virgil Griffith. Casper the friendly finality gadget.arXiv preprint arXiv:1710.09437, 2017
Pith/arXiv arXiv 2017
-
[9]
Díaz-Redondo, and Manuel Fernández-Veiga
Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo, and Manuel Fernández-Veiga. Byzantine- robust aggregation for securing decentralized federated learning.IEEE Access, 13:190947–190963, 2025
2025
-
[10]
FLTrust: Byzantine-robust federated learning via trust bootstrapping
Xiaoyu Cao, Minghong Fang, Jia Liu, and Neil Zhenqiang Gong. FLTrust: Byzantine-robust federated learning via trust bootstrapping. InProceedings of the Network and Distributed System Security Symposium (NDSS), 2021
2021
-
[11]
Polygraph: Accountable Byzantine agreement
Pierre Civit, Seth Gilbert, and Vincent Gramoli. Polygraph: Accountable Byzantine agreement. InProceedings of the 41st IEEE International Conference on Distributed Computing Systems (ICDCS), pages 403–413, 2021
2021
-
[12]
Marczak, Peter Alvaro, Joseph M
Neil Conway, William R. Marczak, Peter Alvaro, Joseph M. Hellerstein, and David Maier. Logic and lattices for distributed programming. InProceedings of the 3rd ACM Symposium on Cloud Computing (SoCC), 2012
2012
-
[13]
Accountability and reconfiguration: Self-healing lattice agreement
Luciano Freitas de Souza, Petr Kuznetsov, Thibault Rieutord, and Sara Tucci-Piergiovanni. Accountability and reconfiguration: Self-healing lattice agreement. In25th International Conference on Principles of Distributed Systems (OPODIS), volume 217 ofLIPIcs, pages 25:1–25:23, 2021
2021
-
[14]
Lynch, Shlomit S
Danny Dolev, Nancy A. Lynch, Shlomit S. Pinter, Eugene W. Stark, and William E. Weihl. Reaching approximate agreement in the presence of faults.Journal of the ACM, 33(3):499–516, 1986
1986
-
[15]
Arthur Douillard, Qixuan Feng, Andrei A. Rusu, Rachita Chhaparia, Yani Donchev, Adhiguna Kuncoro, Marc’Aurelio Ranzato, Arthur Szlam, and Jiajun Shen. DiLoCo: Distributed low-communication training of language models, 2023. arXiv preprint arXiv:2311.08105
Pith/arXiv arXiv 2023
-
[16]
Byzantine-robust federated learning over ring-all-reduce distributed computing
Minghong Fang, Zhuqing Liu, Xuecen Zhao, and Jia Liu. Byzantine-robust federated learning over ring-all-reduce distributed computing. InCompanion Proceedings of the ACM Web Conference 2025 (WWW Companion), pages 961–965, 2025
2025
-
[17]
Byzantine-robust decentralized federated learning
Minghong Fang, Zifan Zhang, Hairi, Prashant Khanduri, Jia Liu, Songtao Lu, Yuchen Liu, and Neil Zhenqiang Gong. Byzantine-robust decentralized federated learning. InProceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS), 2024
2024
-
[18]
Victoria Luzón, and Francisco Herrera
Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón, and Francisco Herrera. Krum federated chain (KFC): Using blockchain to defend against adversarial attacks in federated learning.arXiv preprint arXiv:2502.06917, 2025
Pith/arXiv arXiv 2025
-
[19]
Ryan Gillespie. Conflict-free replicated data types for neural network model merging: A two-layer architecture enabling CRDT-compliant model merging across 26 strategies, 2026. arXiv preprint arXiv:2605.19373. 13
Pith/arXiv arXiv 2026
-
[20]
Recursive trust-as-data binding for byzantine fault tolerant conflict-free replicated data type synchronisation, 2026
Ryan Gillespie. Recursive trust-as-data binding for byzantine fault tolerant conflict-free replicated data type synchronisation, 2026. UK Patent Application GB2608127.3; implemented in thecrdt-mergelibrary v0.9.5+
2026
-
[21]
What every computer scientist should know about floating-point arithmetic.ACM Computing Surveys, 23(1):5–48, 1991
David Goldberg. What every computer scientist should know about floating-point arithmetic.ACM Computing Surveys, 23(1):5–48, 1991
1991
-
[22]
PeerReview: Practical accountability for distributed systems
Andreas Haeberlen, Petr Kuznetsov, and Peter Druschel. PeerReview: Practical accountability for distributed systems. InProceedings of the 21st ACM SIGOPS Symposium on Operating Systems Principles (SOSP), pages 175–188, 2007
2007
-
[23]
Hellerstein and Peter Alvaro
Joseph M. Hellerstein and Peter Alvaro. Keeping CALM: When distributed consistency is easy.Communications of the ACM, 63(9):72–81, 2020
2020
-
[24]
Nicholas J. Higham. The accuracy of floating point summation.SIAM Journal on Scientific Computing, 14(4):783–799, 1993
1993
-
[25]
Florian Jacob, Saskia Bayreuther, and Hannes Hartenstein. On conflict-free replicated data types and equivocation in Byzantine setups.arXiv preprint arXiv:2109.10554, 2021
Pith/arXiv arXiv 2021
-
[26]
Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Kallista Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Hubert Eichner, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Cha...
2021
-
[27]
Making CRDTs Byzantine fault tolerant
Martin Kleppmann. Making CRDTs Byzantine fault tolerant. InProceedings of the 9th Workshop on Principles and Practice of Consistency for Distributed Data (PaPoC), pages 8–15, 2022
2022
-
[28]
Stich, and Martin Jaggi
Anastasia Koloskova, Sebastian U. Stich, and Martin Jaggi. Decentralized stochastic optimization and gossip algorithms with compressed communication. InProceedings of the 36th International Conference on Machine Learning (ICML), volume 97, pages 3478–3487, 2019
2019
-
[29]
Lindsey Kuper and Ryan R. Newton. LVars: Lattice-based data structures for deterministic parallelism. In Proceedings of the 2nd ACM SIGPLAN Workshop on Functional High-Performance Computing (FHPC), pages 71–84, 2013
2013
-
[30]
Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent
Xiangru Lian, Ce Zhang, Huan Zhang, Cho-Jui Hsieh, Wei Zhang, and Ji Liu. Can decentralized algorithms outperform centralized algorithms? A case study for decentralized parallel stochastic gradient descent. In Advances in Neural Information Processing Systems 30 (NeurIPS), 2017
2017
-
[31]
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas
H. Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Agüera y Arcas. Communication- efficient learning of deep networks from decentralized data. InProceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS), pages 1273–1282, 2017
2017
-
[32]
The hidden vulnerability of distributed learning in Byzantium
El Mahdi El Mhamdi, Rachid Guerraoui, and Sébastien Rouault. The hidden vulnerability of distributed learning in Byzantium. InProceedings of the 35th International Conference on Machine Learning (ICML), pages 3521–3530, 2018
2018
-
[33]
Rundungsfehleranalyse einiger verfahren zur summation endlicher summen.Zeitschrift für Angewandte Mathematik und Mechanik (ZAMM), 54(1):39–51, 1974
Arnold Neumaier. Rundungsfehleranalyse einiger verfahren zur summation endlicher summen.Zeitschrift für Angewandte Mathematik und Mechanik (ZAMM), 54(1):39–51, 1974
1974
-
[34]
Improve accuracy of builtin sum() for float inputs (compensated summation, CPython 3.12)
Python Software Foundation. Improve accuracy of builtin sum() for float inputs (compensated summation, CPython 3.12). CPython issue 100425, github.com/python/cpython/issues/100425, 2022
2022
-
[35]
Merkle-CRDTs: Merkle-DAGs meet CRDTs.arXiv preprint arXiv:2004.00107, 2020
Hector Sanjuan, Samuli Poyhtari, Pedro Teixeira, and Ioannis Psaras. Merkle-CRDTs: Merkle-DAGs meet CRDTs.arXiv preprint arXiv:2004.00107, 2020. 14
Pith/arXiv arXiv 2004
-
[36]
A comprehensive study of convergent and commutative replicated data types
Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. A comprehensive study of convergent and commutative replicated data types. Technical Report RR-7506, INRIA, 2011
2011
-
[37]
Conflict-free replicated data types
Marc Shapiro, Nuno Preguiça, Carlos Baquero, and Marek Zawirski. Conflict-free replicated data types. In Proceedings of the 13th International Symposium on Stabilization, Safety, and Security of Distributed Systems (SSS), volume 6976 ofLecture Notes in Computer Science, pages 386–400. Springer, 2011
2011
-
[38]
Manipulating the Byzantine: Optimizing model poisoning attacks and defenses for federated learning
Virat Shejwalkar and Amir Houmansadr. Manipulating the Byzantine: Optimizing model poisoning attacks and defenses for federated learning. InProceedings of the Network and Distributed System Security Symposium (NDSS), 2021
2021
-
[39]
Eventually consistent.Communications of the ACM, 52(1):40–44, 2009
Werner Vogels. Eventually consistent.Communications of the ACM, 52(1):40–44, 2009
2009
-
[40]
Byzantine-resilient decentralized stochastic optimization with robust aggregation rules.IEEE Transactions on Signal Processing, 71:3179–3195, 2023
Zhaoxian Wu, Tianyi Chen, and Qing Ling. Byzantine-resilient decentralized stochastic optimization with robust aggregation rules.IEEE Transactions on Signal Processing, 71:3179–3195, 2023
2023
-
[41]
Bartlett
Dong Yin, Yudong Chen, Kannan Ramchandran, and Peter L. Bartlett. Byzantine-robust distributed learning: Towards optimal statistical rates. InProceedings of the 35th International Conference on Machine Learning (ICML), pages 5650–5659, 2018. A Falsifier Transcript Summary The battery runs aspython3 falsifier.py (CPU only; about four seconds on commodity h...
2018
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