Paper Citation Record · LEDGER

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML

As of 11 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2501.09621.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.09621 v2

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T20:10:43.689753Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3074be22-59f7-42c7-a388-ac7313c0ddb9 · outbound

This paper cites Robust training in high dimensions via block coordinate geometric median descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Robust training in high dimensions via block coordinate geometric median descent

Reference 1

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Observation 852e1621-ab87-49bb-943d-c682c9f447ef · outbound

This paper cites Byzantine stochastic gradient descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine stochastic gradient descent

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:44.600547Z

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Observation 23811c01-b92a-4aa5-86d9-2c4278582225 · outbound

This paper cites Byzantine-Resilient Non-Convex Stochastic Gradient Descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine-Resilient Non-Convex Stochastic Gradient Descent

Reference 3

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Observation 17a25422-6399-4452-adc2-57786619250d · outbound

This paper cites Fixing by mixing: A recipe for optimal byzantine ml under heterogeneity.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Fixing by mixing: A recipe for optimal byzantine ml under heterogeneity

Reference 4

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Observation f01e5eb3-befb-41ee-b470-7bb87e1fab34 · outbound

This paper cites A tight convergence analysis for stochastic gradient descent with delayed updates.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML A tight convergence analysis for stochastic gradient descent with delayed updates

Reference 5

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Observation 0f04219f-fab4-4ced-9913-c8b0cc59cf10 · outbound

This paper cites Asynchronous distributed learning: Adapting to gradient delays without prior knowledge.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous distributed learning: Adapting to gradient delays without prior knowledge

Reference 6

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Observation d48f9259-209a-4b34-8127-93ac20997c60 · outbound

This paper cites A little is enough: Circumventing defenses for distributed learning.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML A little is enough: Circumventing defenses for distributed learning

Reference 7

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Observation e61cc54f-0ed5-4365-bbe4-645c0021c5c8 · outbound

This paper cites Machine learning with adversaries: Byzantine tolerant gradient descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Machine learning with adversaries: Byzantine tolerant gradient descent

Reference 8

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Observation fedbe929-836d-48c6-a36a-69eaa5b14c17 · outbound

This paper cites Distributed statistical machine learning in adversarial settings: Byzantine gradient descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Distributed statistical machine learning in adversarial settings: Byzantine gradient descent

Reference 9

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Observation 00c281a1-44c7-416b-aad9-0fc76dd9cee2 · outbound

This paper cites Asynchronous stochastic optimization robust to arbitrary delays.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous stochastic optimization robust to arbitrary delays

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:44.451517Z

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Observation 2a87d5a2-c438-4830-9461-ac58e5c6f849 · outbound

This paper cites Anytime online-to-batch, optimism and acceleration.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Anytime online-to-batch, optimism and acceleration

Reference 11

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Observation c2b6f27e-9457-4185-9e6e-ee46460994ad · outbound

This paper cites Momentum-based variance reduction in non-convex sgd.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Momentum-based variance reduction in non-convex sgd

Reference 12

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Observation 681f37b6-568c-4772-8a33-1d8941e887ab · outbound

This paper cites Fault tolerant ml: Efficient meta-aggregation and synchronous training.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Fault tolerant ml: Efficient meta-aggregation and synchronous training

Reference 13

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Observation d89bd4b5-63ae-47ce-9906-2d8255a9db38 · outbound

This paper cites Asynchronous byzantine machine learning (the case of sgd).

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous byzantine machine learning (the case of sgd)

Reference 14

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Observation 760e5692-fef9-4db0-afcc-277ddbd5d134 · outbound

This paper cites Optimal distributed online prediction using mini-batches.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Optimal distributed online prediction using mini-batches

Reference 15

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Observation f655c7b5-8f81-43f6-800c-94edec8cf88f · outbound

This paper cites Distributed momentum for byzantine-resilient stochastic gradient descent.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Distributed momentum for byzantine-resilient stochastic gradient descent

Reference 16

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Observation 240a6873-e1fe-4886-aeec-26c7545212ec · outbound

This paper cites Online learning and stochastic approximations.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Online learning and stochastic approximations

Reference 17

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Observation 53c73060-2821-4e0c-8403-251395697bee · outbound

This paper cites Aflguard: Byzantine-robust asynchronous federated learning.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Aflguard: Byzantine-robust asynchronous federated learning

Reference 18

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Observation 6d3b7b9e-8d71-41a5-a359-3157769e2a82 · outbound

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Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine machine learning made easy by resilient averaging of momentums

Reference 19

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Observation a06ddaf7-5034-4f9a-b29d-a105aad490f9 · outbound

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Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML The hidden vulnerability of distributed learning in byzantium

Reference 20

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Observation 8e766cc5-7b08-40dd-8abe-a6d7e5d1b0de · outbound

This paper cites Byzantine machine learning: A primer.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine machine learning: A primer

Reference 21

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Observation 54dae628-ed7b-41ff-b1c5-a3f89573bce6 · outbound

This paper cites Introduction to online convex optimization.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Introduction to online convex optimization

Reference 22

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This paper cites Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing

Reference 23

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Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Learning from history for byzantine robust optimization

Reference 24

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Observation bb2cc015-3f0c-4fc4-bde5-2509fdd0b28d · outbound

This paper cites Unixgrad: A universal, adaptive algorithm with optimal guarantees for constrained optimization.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Unixgrad: A universal, adaptive algorithm with optimal guarantees for constrained optimization

Reference 25

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Observation 9c835be1-5c19-4a41-830f-aaf8f0bf79c2 · outbound

This paper cites The cifar-10 dataset.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML The cifar-10 dataset

Reference 26

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Observation c0b9da89-715a-4068-9ea9-61d771270256 · outbound

This paper cites The byzantine generals problem.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML The byzantine generals problem

Reference 27

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This paper cites Slow Learners are Fast.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Slow Learners are Fast

Reference 28

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This paper cites Mnist handwritten digit database, 2010.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Mnist handwritten digit database, 2010

Reference 29

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This paper cites $\mu^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML $\mu^2$-SGD: Stable Stochastic Optimization via a Double Momentum Mechanism

Reference 30

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Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous sgd beats minibatch sgd under arbitrary delays

Reference 31

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Observation dce3318c-019f-453d-b06e-e73f89d03eb7 · outbound

This paper cites Some methods of speeding up the convergence of iteration methods.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Some methods of speeding up the convergence of iteration methods

Reference 32

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Observation 7fa0e1ab-6e9f-470c-b19b-1f6c11bc3f21 · outbound

This paper cites The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML The Error-Feedback Framework: Better Rates for SGD with Delayed Gradients and Compressed Communication

Reference 33

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This paper cites Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Fall of empires: Breaking byzantine-tolerant sgd by inner product manipulation

Reference 34

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This paper cites Zeno++: Robust fully asynchronous sgd.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Zeno++: Robust fully asynchronous sgd

Reference 35

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Observation c810f3e9-9587-4ed1-9a5e-2481adc59a19 · outbound

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Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Basgd: Buffered asynchronous sgd for byzantine learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:43.978044Z

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Observation 191839d7-69fa-4d9d-9f60-e8864793f35e · outbound

This paper cites Buffered asynchronous sgd for byzantine learning.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Buffered asynchronous sgd for byzantine learning

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:43.957895Z

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Observation 59d81b98-b7e5-4a4c-8ae9-234c516d2477 · outbound

This paper cites Byzantine-robust distributed learning: Towards optimal statistical rates.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Byzantine-robust distributed learning: Towards optimal statistical rates

Reference 38

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This paper cites A Survey of Large Language Models.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML A Survey of Large Language Models

Reference 39

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This paper cites Asynchronous byzantine-robust stochastic aggregation with variance reduction for distributed learning.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous byzantine-robust stochastic aggregation with variance reduction for distributed learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:43.905867Z

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Observation a2d0d8c0-a55c-47dc-8213-dece72b088d3 · outbound

This paper cites Asynchronous byzantine-robust stochastic aggregation with variance reduction for distributed learning.

Weight for Robustness: A Comprehensive Approach towards Optimal Fault-Tolerant Asynchronous ML Asynchronous byzantine-robust stochastic aggregation with variance reduction for distributed learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T20:10:43.881444Z

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