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Paper Citation Record · LEDGER

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization

As of 23 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 1 inbound Pith citation observation for arXiv:2412.02781.

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

pith.paper-citation-record.v1
2412.02781 v3

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T23:14:14.279406Z

measured 21 of 21 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:39:14.458028Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-15T14:39:16.584831Z

Reference resolution

20 of 20 outbound references displayed

  • verified exact0
  • verified fuzzy13
  • unresolved7
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 33f1d63f-7263-460e-881d-c615b1ee80b0 · outbound

This paper cites For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L1 ∇f ˆxtp.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L1 ∇f ˆxtp

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.969221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.212251Z digest=sha256:e20de34e02f8fd82c00c1a77bb95a2bce4c76c4acc7f5f628bf5451b6dc54957

Observation 8ad0ac15-732f-453d-a1a3-eed54918beb5 · outbound

This paper cites For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L0.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L0

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.945747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.218791Z digest=sha256:aeeeeddfee4d04c4a96b0959e7f2fed4e9f266be4777fbcb486c79588f217632

Observation 4e71fd5e-5caf-4e69-96fa-0992afcc3372 · outbound

This paper cites 16 Published as a conference paper at ICLR 2025 Quoc Tran-Dinh, Nhan H.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization 16 Published as a conference paper at ICLR 2025 Quoc Tran-Dinh, Nhan H

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.198657Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.198657Z digest=sha256:8aa4c5d829cde63f0df785216a284dbcb1ddeafe3ef3503f9b095ff2d5ce109c

Observation a9fb8679-df8a-4576-aed8-5cd5073e8b64 · outbound

This paper cites Yang You, Jing Li, Sashank J.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Yang You, Jing Li, Sashank J

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.206364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.206364Z digest=sha256:6d45db391f67dc0f766db98e989bf0d1df291490cc1804bf11ba95f689376e0b

Observation 7703a73c-785d-46d2-b29c-38dbd98dc586 · outbound

This paper cites CLERR clips outer gradients at the level of 10, so this also does not help method to converge to a better area.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization CLERR clips outer gradients at the level of 10, so this also does not help method to converge to a better area

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.744002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.272787Z digest=sha256:777dd210ba52e91ebf6511dbb7368f3ca707a8b7e07c82d26b444bc4f2322920

Observation cd16b171-95d5-4f24-a679-720d1026bdf4 · outbound

This paper cites For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L1 ∇f ˆxtp.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L1 ∇f ˆxtp

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.924623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.224436Z digest=sha256:cecc616adef54e9a63162c2693fc215088d505ed1dfbc0f9a80ac84eeb52b42e

Observation d0c5c5ed-87dc-49a2-a0fc-2f73d77e4410 · outbound

This paper cites For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L0.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such p, we have L0 + L1 ∇f ˆxtp = ˆap ≤ 2L0

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.902554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.232071Z digest=sha256:4dffef0a4d11ce05b704708492ecbbc82fb578c5e180bd40f57cc69de4703445

Observation 5b6871ef-c31d-404f-b914-559ed3da8038 · outbound

This paper cites For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.878997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.238776Z digest=sha256:1c738e4f8cbe447cd7be91c005a18859135520e403fa7aae0d5bcd7a953e181d

Observation 1e101401-e255-46c4-bb20-bb2d2d197f4b · outbound

This paper cites For such t, we have L0 +L1 ∥∇f (xt)∥ = ˆap ≤ 2L0.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such t, we have L0 +L1 ∥∇f (xt)∥ = ˆap ≤ 2L0

Reference 14

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.854846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.244805Z digest=sha256:0e509a36aef0f7eea0981cc66397f2d48abca5a48fad2775689935a0fca2f986

Observation f3a74ace-1a97-44bf-a03e-21b3db54e83a · outbound

This paper cites For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.829151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.250825Z digest=sha256:844908888cd08b47e1ac7b891aed012b9c9da04c97b3589836aae2c124cc9bd5

Observation b5fbc22f-8f78-4133-ae53-6febca143ec3 · outbound

This paper cites For such t, we have L0 +L1 ∥∇f (xt)∥ = ˆap ≤ 2L0.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such t, we have L0 +L1 ∥∇f (xt)∥ = ˆap ≤ 2L0

Reference 16

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.806509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.256643Z digest=sha256:d6496cf7bc2c9b97dfd5930608e5a63d17a974767e2c079ad86cfb21f15f1400

Observation 75a21bfd-62e9-4043-ae19-3aeb51157258 · outbound

This paper cites For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization For such t, we haveL0+L1 ∥∇f (xt)∥ = ˆat ≤ 2L1 ∥∇f (xt)∥

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.785712Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.262393Z digest=sha256:a26bd49b353aa2eedbb2fc07d972dda052aa22fd64833dee763df347d09241cc

Observation 3417bafa-6925-4b22-802e-be95e58bbf4c · outbound

This paper cites practical.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization practical

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.765924Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.267694Z digest=sha256:7c4accb7460ca709f6e2811f9472fa0a8ee02c075e0dd2191a0ee4f2041b1313

Observation 08ae261f-991c-46eb-acff-9fb20dace47c · outbound

This paper cites And CE-FedAvg-PP has server stepsize equal to 10.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization And CE-FedAvg-PP has server stepsize equal to 10

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.723784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.279406Z digest=sha256:503fdf581816b5220f9aca96992c5f3730c58772e2f0de690c361dbca10fd63a

Observation 0c2b73f2-1cca-44c3-bade-798ecf948f24 · outbound

This paper cites ISBN 9781450312851.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization ISBN 9781450312851

Reference 2012

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.192439Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.192439Z digest=sha256:53ea82bc1c3610375c47d63cfc828fba9af90c623d523d54816b04bcfe44f3d5

Observation c099227c-c69f-475b-9080-d8223768b799 · outbound

This paper cites Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Reference 2019

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.185445Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.185445Z digest=sha256:36139cead893c9533248a688fff0d3897e7d761b247574772805d89c7ccf9dab

Observation 9c638d52-4af4-475d-a78b-a9d32db925d6 · outbound

This paper cites Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds

Reference 2020

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.166490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.166490Z digest=sha256:99187d2e3418d01d49ff939430d51a88bc4b0aee1e833b3e63761a9eeb335581

Observation ad9f0613-c233-4879-995c-7d82f9b045e1 · outbound

This paper cites Wenlin Chen, Samuel Horv ´ath, and Peter Richt ´arik.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Wenlin Chen, Samuel Horv ´ath, and Peter Richt ´arik

Reference 2021

Resolution
verified fuzzy
raw_fallback, observed 2026-08-11T23:14:14.989350Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-11T23:14:14.172446Z digest=sha256:b3cc44fe2b04be76995c28ec8d971229e1aa039460c69f6f40bda53203c165eb

Observation 0a89b6cd-f1de-4bc7-ab54-df690e515807 · outbound

This paper cites Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Methods for Convex $(L_0,L_1)$-Smooth Optimization: Clipping, Acceleration, and Adaptivity

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.178862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.178862Z digest=sha256:810212cf57c08c26bfde308910412b5fcf73b97a95797f2ba6f94803531760e4

Observation 02527fbd-c341-4fb1-b841-55481cfc693f · outbound

This paper cites Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey.

Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization Incremental Gradient, Subgradient, and Proximal Methods for Convex Optimization: A Survey

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-11T23:14:14.158702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T23:14:14.158702Z digest=sha256:871d7eac53a2fa85aaab97a17458b27c1742aea951ebb43bf180d1f9885104da

Pith citing papers

Observation 3a9c108e-17e0-4d89-ae90-aadf73cd8384 · inbound

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization cites this paper.

Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization Methods with Local Steps and Random Reshuffling for Generally Smooth Non-Convex Federated Optimization

Reference 70

Resolution
verified exact
local_arxiv, observed 2026-08-15T14:39:16.588570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

source=pdf_text observed=2026-08-15T14:39:14.458028Z digest=sha256:a002540180f01ec0eddaba2354692abe66b3e6bec9707faf981b04a58f8d1276