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Paper Citation Record · LEDGER

Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2002.10940.

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

pith.paper-citation-record.v1
2002.10940 v5

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:12:57.757143Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T14:13:21.237419Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 18de87ef-29fd-40ad-a805-85677420daf0 · inbound

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness cites this paper.

Sign Operator for Coping with Heavy-Tailed Noise in Non-Convex Optimization: High Probability Bounds Under $(L_0, L_1)$-Smoothness Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-08T11:35:29.665720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T11:35:29.665720Z digest=sha256:433f4c33dd11fbb3cb80dc40c0fdcfa81727ce0cfb24917dd2c7c22f5dbb83f6

Observation 56227bfa-efad-4160-831f-ac4850bb8a40 · inbound

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning cites this paper.

Privacy Preserving Machine Learning Model Personalization through Federated Personalized Learning Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-16T04:12:57.757143Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T04:12:57.757143Z digest=sha256:91ee63873f17b72e26be2d207fb5e6732982e771f3a54a5b2d6c1d57b81fc76e

Observation e443549c-b651-4ca3-9450-c1c3f949e365 · inbound

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates cites this paper.

Byzantine Outside, Curious Inside: Reconstructing Data Through Malicious Updates Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 71

Resolution
unresolved
no resolver link, observed 2026-08-07T04:17:46.792095Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:17:46.792095Z digest=sha256:2bf9c79e34991cd987bc5c73f746e225895a6e6ca6e06a9e9b157a761e2d4935

Observation 2ae7a804-2df4-437d-a84c-c889d1ce153e · inbound

Centroid Approximation for Byzantine-Tolerant Federated Learning cites this paper.

Centroid Approximation for Byzantine-Tolerant Federated Learning Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-15T20:00:30.019564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:00:30.019564Z digest=sha256:c32d9409d9bb757a044f354afd9bdb735b409c66136c4bab445ac0281d028a77

Observation 6c20472c-8c0f-4fdf-93ef-71f74f3ce58f · inbound

Improved Analysis for Sign-based Methods with Momentum Updates cites this paper.

Improved Analysis for Sign-based Methods with Momentum Updates Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 15

Resolution
unresolved
no resolver link, observed 2026-08-06T17:13:44.717529Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T17:13:44.717529Z digest=sha256:4d29c6999e958e09fda32a959001474c3402c5e0b2e9685011b695d249da3c23

Observation 196af736-2878-4d1a-b4c8-22092e7acff2 · inbound

Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings cites this paper.

Convergence Analysis of the Lion Optimizer in Centralized and Distributed Settings Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-15T17:35:15.205727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T17:35:15.205727Z digest=sha256:6b9ee3a0df935b259c37bdcf197ec477278ef7a6f00a309c79394b61e6f6d76c

Observation 9a82bb99-8359-499e-a71a-0864560b9994 · inbound

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models cites this paper.

ParaBlock: Communication-Computation Parallel Block Coordinate Federated Learning for Large Language Models Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-03T20:29:49.125473Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T20:29:49.125473Z digest=sha256:4cb57b10e4590b6165ad271b41e9a5a12d351891cfbbbc747018a8e39e36d906

Observation 746ed81a-b507-4d46-8694-eb50005040d2 · inbound

Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning cites this paper.

Q-LocalAdam: Memory-Efficient Client-Side Adaptive Optimization for Edge Federated Learning Stochastic-Sign SGD for Federated Learning with Theoretical Guarantees

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:13:21.238755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-20T14:11:53.371521Z digest=sha256:51e115fdc359cdcd1c8cc805e0835374f61d84cbe9f5b28b8b6b8f90dcc0fcb9