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Paper Citation Record · LEDGER

Local SGD Converges Fast and Communicates Little

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 43 inbound Pith citation observations for arXiv:1805.09767.

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

pith.paper-citation-record.v1
1805.09767 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 43 of 43 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 43 of 43 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:05:48.165992Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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External citation measurements

222
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation c8b49888-7de9-4398-852c-a9162dee8530 · inbound

Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training cites this paper.

Hardware Scaling Trends and Diminishing Returns in Large-Scale Distributed Training Local SGD Converges Fast and Communicates Little

Reference 18

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source=pdf_text observed=2026-08-12T16:57:08.252150Z digest=sha256:9ffaba1d5c75f7afae621c18cc7162a25f0106b51013457d84620eae061ab64f

Observation 53fd3733-51e9-4b0d-bc6d-c6f1d4c4b2c5 · inbound

Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel cites this paper.

Asynchronous Federated Learning Using Outdated Local Updates Over TDMA Channel Local SGD Converges Fast and Communicates Little

Reference 3

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source=pdf_text observed=2026-08-12T15:53:21.585707Z digest=sha256:e183babddc157131391773bc81515e44c7a350435a8c65a898c7199c69de3f2b

Observation f323f905-a048-488b-bfd0-aa2ff7383e44 · inbound

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression cites this paper.

Local Steps Speed Up Local GD for Heterogeneous Distributed Logistic Regression Local SGD Converges Fast and Communicates Little

Reference 31

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source=arxiv_source observed=2026-08-10T15:46:47.700925Z digest=sha256:2c4b75ed9a331cfc887b3498583de6ee5b7f5810c683ac573bb35768c4958200

Observation 6e6d0775-f69f-4d66-b51d-20a1e4686452 · inbound

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch cites this paper.

Streaming DiLoCo with overlapping communication: Towards a Distributed Free Lunch Local SGD Converges Fast and Communicates Little

Reference 43

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source=pdf_text observed=2026-08-09T23:17:19.851728Z digest=sha256:d3c963131ee7affa1d392bf654993fa7de9865411cb87075af797ade80eecc93

Observation d16211cd-049a-426d-98ec-920c9391e652 · inbound

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning cites this paper.

Coreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning Local SGD Converges Fast and Communicates Little

Reference 1972

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source=pdf_text observed=2026-08-09T12:41:16.304744Z digest=sha256:778dc7f0dd5e501e5f5d4575710ba104cc7e5fdc2f7ed10999e338bd6ed097d0

Observation d272633e-84c3-4551-9f32-b3ad7aaeb6fd · inbound

Efficient Distributed Optimization under Heavy-Tailed Noise cites this paper.

Efficient Distributed Optimization under Heavy-Tailed Noise Local SGD Converges Fast and Communicates Little

Reference 2022

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source=pdf_text observed=2026-08-08T23:25:10.639899Z digest=sha256:41559841bbf05a931af96933a98e7169db69f42c47b9504ef44ca3eaf4258ded

Observation 24f1d104-4946-483c-8b97-0eedb456d05d · inbound

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks cites this paper.

Aequa: Fair Model Rewards in Collaborative Learning via Slimmable Networks Local SGD Converges Fast and Communicates Little

Reference 41

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source=arxiv_source observed=2026-08-08T21:19:39.365877Z digest=sha256:b1e071ee22466a942ef428ea5ac72aef28de165def5b2e401b27997649b65cbb

Observation ab9b8164-5f4b-4dd0-bedc-b16867ce0281 · inbound

Memory-Efficient Distributed Unlearning cites this paper.

Memory-Efficient Distributed Unlearning Local SGD Converges Fast and Communicates Little

Reference 46

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source=pdf_text observed=2026-08-15T23:58:55.262560Z digest=sha256:c7602cbbb109eac69a7d07b565348ff924108b33e1e31c350e85767e80406e86

Observation 8143b00d-123b-4278-ac68-60abc1fca35e · inbound

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments cites this paper.

Cluster-Aware Multi-Round Update for Wireless Federated Learning in Heterogeneous Environments Local SGD Converges Fast and Communicates Little

Reference 13

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source=pdf_text observed=2026-08-16T00:05:48.165992Z digest=sha256:089dbe4da77047d72da3ede5324dd39a5cd453a893de0f9d9917786a0036c3e4

Observation 8ba79a71-f9ed-4ec2-b28a-5e96becd7255 · inbound

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum cites this paper.

Convergence Analysis of the Last Iterate in Distributed Stochastic Gradient Descent with Momentum Local SGD Converges Fast and Communicates Little

Reference 11

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source=pdf_text observed=2026-08-15T21:16:04.939109Z digest=sha256:d37a35f09770eb95292617f3f21b6a7aeb834ac062034773fef29a4bdfdfdbea

Observation 37d89998-8e98-4329-b022-b5ed1fd3078e · inbound

FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments cites this paper.

FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments Local SGD Converges Fast and Communicates Little

Reference 34

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source=pdf_text observed=2026-08-15T20:23:43.892988Z digest=sha256:14a0b4f5b71d6de8c4ced2bd1e07deb95325f39114b556bd1431103f66920d51

Observation 4a6de098-8d2b-4bbb-9db4-8b7d89d054fd · inbound

Distributionally Robust Federated Learning with Client Drift Minimization cites this paper.

Distributionally Robust Federated Learning with Client Drift Minimization Local SGD Converges Fast and Communicates Little

Reference 2019

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source=pdf_text observed=2026-08-07T15:25:37.648396Z digest=sha256:0ab12855c323b8b7206b3ac006cb34b907b08ea316b2852c367feee9a1e8d752

Observation bb76f176-6b95-47ec-8850-2e40aa598c17 · inbound

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models cites this paper.

DeCAF: Decentralized Consensus-And-Factorization for Low-Rank Adaptation of Foundation Models Local SGD Converges Fast and Communicates Little

Reference 52

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source=pdf_text observed=2026-08-07T13:42:49.463427Z digest=sha256:8f441e5b2a75c800d20dc1fd78ed46959c882257a0063fba64db030eca778a22

Observation 9247b31c-aa35-441b-8dfc-1e52c6e69505 · inbound

MuLoCo: Muon is a practical inner optimizer for DiLoCo cites this paper.

MuLoCo: Muon is a practical inner optimizer for DiLoCo Local SGD Converges Fast and Communicates Little

Reference 49

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source=arxiv_source observed=2026-08-07T12:45:29.253512Z digest=sha256:76bf29f361f381396731a4d4930d6211e39531339ed8ce32b9ef11a049d77ed0

Observation 41e14b9c-bb69-441b-98c4-409f10321b72 · inbound

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs cites this paper.

AutoMixAlign: Adaptive Data Mixing for Multi-Task Preference Optimization in LLMs Local SGD Converges Fast and Communicates Little

Reference 43

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source=arxiv_source observed=2026-08-07T12:08:32.950210Z digest=sha256:842587f614f548f753caa975aa796eb2f15709133e0cd71482a0d7027bc317df

Observation 699cca50-5590-4211-bf8c-18eb009170d8 · inbound

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training cites this paper.

HALoS: Hierarchical Asynchronous Local SGD over Slow Networks for Geo-Distributed Large Language Model Training Local SGD Converges Fast and Communicates Little

Reference 41

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source=arxiv_source observed=2026-08-07T10:46:06.600071Z digest=sha256:9baaaa0a3b964c2b0c12f1a6b4692bfbb9bb63150ccaf93b8764fce35df88eed

Observation 44255072-9503-43ee-86c6-83283ef7f9e0 · inbound

Incentivizing High-quality Participation From Federated Learning Agents cites this paper.

Incentivizing High-quality Participation From Federated Learning Agents Local SGD Converges Fast and Communicates Little

Reference 39

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source=arxiv_source observed=2026-08-15T19:26:45.617905Z digest=sha256:4d28d9148917355790cfaed13f43289ae72340772366335ae433c0c484c5ff22

Observation 162f89b9-3147-475b-a8d1-239e65df1938 · inbound

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness cites this paper.

What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness Local SGD Converges Fast and Communicates Little

Reference 135

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source=pdf_text observed=2026-08-06T21:27:28.185999Z digest=sha256:2b29a3b04e2cb25d15a9cc431a798df834381b6ee72c421b7abf946cd65a5445

Observation cc2507b9-d7f1-425d-8137-783bfc711ad2 · inbound

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints cites this paper.

A Multi-Objective Optimization framework for Decentralized Learning with coordination constraints Local SGD Converges Fast and Communicates Little

Reference 26

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source=pdf_text observed=2026-08-06T16:19:48.006229Z digest=sha256:66403dfee455286007f608b06cdc4e3615a77fdc2a75ae53bca26f9e170065ca

Observation bcd5c673-3b63-4930-ba04-41fde54fe9d0 · inbound

Communication-Efficient Decentralized Stochastic Minimax Optimization cites this paper.

Communication-Efficient Decentralized Stochastic Minimax Optimization Local SGD Converges Fast and Communicates Little

Reference 47

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source=pdf_text observed=2026-08-06T12:22:29.508136Z digest=sha256:01c1b753c06f5e124a2a684c82c0cb266cef8733dbc3753fa3d5a2e464363593

Observation 7cb37444-c8f2-43fd-9dba-90dcb88ef10d · inbound

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach cites this paper.

Federated Learning on Riemannian Manifolds: A Gradient-Free Projection-Based Approach Local SGD Converges Fast and Communicates Little

Reference 34

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source=arxiv_source observed=2026-08-06T11:24:43.262290Z digest=sha256:46087d6c529e6fe701709baf3abe5d3755998f34499ea2e2a4828735f7d17653

Observation cfda8956-9216-4fbf-abad-c3a3e96ce514 · inbound

Overcoming the Communication-Performance Tradeoff in LLM Pretraining cites this paper.

Overcoming the Communication-Performance Tradeoff in LLM Pretraining Local SGD Converges Fast and Communicates Little

Reference 25

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source=pdf_text observed=2026-08-05T17:50:42.825545Z digest=sha256:391874dc041b929d46c4772d473bdf27c84b9d27cd20f3f02a71ee9ae00489d5

Observation d83783d3-33d3-4d47-a3f5-abf2197918f1 · inbound

AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models cites this paper.

AdLoCo: adaptive batching significantly improves communications efficiency and convergence for Large Language Models Local SGD Converges Fast and Communicates Little

Reference 7

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source=arxiv_source observed=2026-08-05T16:36:50.529584Z digest=sha256:7dabc36ac1c3589f2f004dda5aab510d9e339bcb164c08415035bb25da67c0e4

Observation f99aa77c-7c29-4f18-b805-43694f710a29 · inbound

Federated learning over physical channels: adaptive algorithms with near-optimal guarantees cites this paper.

Federated learning over physical channels: adaptive algorithms with near-optimal guarantees Local SGD Converges Fast and Communicates Little

Reference 25

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source=arxiv_source observed=2026-08-15T16:44:48.691797Z digest=sha256:c36812d037fb12b24638dfeaedc9838840e54c980b6261fd1b8ff08ef6b8302a

Observation ec62cc81-15a1-4831-bbbe-3a21d56fb815 · inbound

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization cites this paper.

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization Local SGD Converges Fast and Communicates Little

Reference 200

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source=arxiv_source observed=2026-08-04T21:06:26.502347Z digest=sha256:52786c893c6f632a5bfc40672c9025bb603c5d926523b77baa0bb83032ba0d28

Observation ea7c4030-4c06-4afa-81b5-7303a6c385ea · inbound

Paris: A Decentralized Trained Open-Weight Diffusion Model cites this paper.

Paris: A Decentralized Trained Open-Weight Diffusion Model Local SGD Converges Fast and Communicates Little

Reference 25

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source=arxiv_source observed=2026-08-04T12:37:34.570796Z digest=sha256:e979df08d5ceb5d99e48fdf595a8071fac98ddb80faf9f960ffd55d7f3578df9

Observation ecef2603-bcf3-4b72-aeee-6706d80f2d3f · inbound

Adaptive Federated Learning to Optimize Integrated Flows in Cyber-Physical Data Centers cites this paper.

Adaptive Federated Learning to Optimize Integrated Flows in Cyber-Physical Data Centers Local SGD Converges Fast and Communicates Little

Reference 25

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source=pdf_text observed=2026-08-04T00:36:18.873409Z digest=sha256:9887ba830f43d0057f5aa74af6e753cfdf5e73d751e8ce7e4ee499429d10eb0c

Observation 7ce6cbf7-4f72-42b1-b2e7-61b7eae8678f · inbound

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer cites this paper.

Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer Local SGD Converges Fast and Communicates Little

Reference 2023

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source=pdf_text observed=2026-08-15T15:46:21.676342Z digest=sha256:f1e4b5449ae0cc7c2e448599827829f960457f43eed77d0471c4f661a3bd8941

Observation a5bf1491-17b2-432d-a428-e5f37d1b7a1f · inbound

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity cites this paper.

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity Local SGD Converges Fast and Communicates Little

Reference 88

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local_arxiv, observed 2026-05-14T19:32:51.265405Z

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

source=arxiv_source observed=2026-05-14T19:31:12.149482Z digest=sha256:75eca867274349f9a9338428df58d8f12ddf13364b0df2f52ca41dc1b50e1ea4

Observation fade2c1e-6722-4539-8673-6a674ca7037f · inbound

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling cites this paper.

Distributionally Robust Multi-Task Reinforcement Learning via Adaptive Task Sampling Local SGD Converges Fast and Communicates Little

Reference 233

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local_arxiv, observed 2026-05-15T03:08:59.514821Z

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

source=arxiv_source observed=2026-05-15T03:05:36.871497Z digest=sha256:ddf93dff44d84ef9a1486bc7080aae2baf58e0d6f8d92bc6c8b64c73f2aa7ae0

Observation e82cc299-5f45-4dd5-8d53-6fbfcdde9084 · inbound

Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems cites this paper.

Response-Conditioned Parallel-to-Sequential Orchestration for Multi-Agent Systems Local SGD Converges Fast and Communicates Little

Reference 143

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local_arxiv, observed 2026-05-20T19:33:42.317448Z

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source=arxiv_source observed=2026-05-20T19:30:13.469451Z digest=sha256:a22cfeb3a6b0cc2691e05a6f688f86c0c2c59d9efe056a51806bf109650e5a4f

Observation d8400e07-5183-46b5-9048-a425ec418eee · inbound

Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training cites this paper.

Runtime-Orchestrated Second-Order Optimization for Scalable LLM Training Local SGD Converges Fast and Communicates Little

Reference 24

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local_arxiv, observed 2026-05-19T18:32:42.910408Z

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

source=pdf_text observed=2026-05-19T18:30:24.657592Z digest=sha256:f85f1d76297b9a6022d72017cf3805fb70788743379cc358e47bbd70dc4ec926

Observation 4babd5f9-5e37-4d68-bc25-4a3ffa91f4ed · inbound

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning cites this paper.

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning Local SGD Converges Fast and Communicates Little

Reference 12

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local_arxiv, observed 2026-05-20T08:03:08.725405Z

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

source=pdf_text observed=2026-05-20T08:01:27.080031Z digest=sha256:cd8f9efaedd827bfdcadf89e34fbd4e99ee788c0c0439c628d29b0e9bb5b89ef

Observation 10babad4-2274-46e9-87c2-aa4be264a9b0 · inbound

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation cites this paper.

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation Local SGD Converges Fast and Communicates Little

Reference 93

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local_arxiv, observed 2026-05-20T02:12:58.385268Z

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

source=arxiv_source observed=2026-05-20T02:10:02.167114Z digest=sha256:cd49e20ed0c42096e0177b34842da7a2bc357313eb7097e8bcb8d02b312f9eaa

Observation fb9b99e1-b0e7-4a26-8dcd-b459da8ead3c · inbound

Synchronous and Asynchronous Parallelism Approaches for Generalized Canonical Polyadic Tensor Decomposition with GenTen cites this paper.

Synchronous and Asynchronous Parallelism Approaches for Generalized Canonical Polyadic Tensor Decomposition with GenTen Local SGD Converges Fast and Communicates Little

Reference 35

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local_arxiv, observed 2026-05-21T07:19:46.794208Z

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

source=pdf_text observed=2026-05-21T07:18:06.388075Z digest=sha256:7c1e48d6a17ef37a6c799f33d4efd063637b52030577eca514198287729c32c2

Observation c1139629-9f4a-477a-bfd8-6e5a5fb509b9 · inbound

A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling cites this paper.

A Note on Stability for Orthogonalized Matrix Momentum with Client Sampling Local SGD Converges Fast and Communicates Little

Reference 19

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local_arxiv, observed 2026-07-01T21:56:15.682559Z

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

source=pdf_text observed=2026-06-28T16:03:26.866407Z digest=sha256:7cf4725628b99146b0d99460afd3331d75f5c17fe6cab2d3dbe6647b0cd41e5f

Observation 1af8dc1b-cf35-4ca3-8554-cd9f17da91e3 · inbound

Boosting Multimodal Federated Learning via Chained Modality Optimization cites this paper.

Boosting Multimodal Federated Learning via Chained Modality Optimization Local SGD Converges Fast and Communicates Little

Reference 14

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T01:06:24.080528Z

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=2026-06-28T12:50:41.988694Z digest=sha256:23c25b86bbc61b09d47456c1035213e42f0dbfff1c1a1c305ac6f037907daf2b

Observation 72c11270-2e3c-4f1e-bb1e-c5a96b4dc627 · inbound

Demystifying Pipeline Parallelism: First Theory for PipeDream cites this paper.

Demystifying Pipeline Parallelism: First Theory for PipeDream Local SGD Converges Fast and Communicates Little

Reference 7

Resolution
metadata mismatch
local_arxiv, observed 2026-07-02T01:56:28.474776Z

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=arxiv_source observed=2026-06-28T11:21:52.068934Z digest=sha256:162fc089898c64b1f1c9980b8b4fb2fac3ef6fc232613355166b01cc817746ae

Observation b10b393c-768c-4181-94e1-5bbdc0532dae · inbound

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning cites this paper.

From Data Heterogeneity to Convergence: A Data-Centric Review of Federated Learning Local SGD Converges Fast and Communicates Little

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-07-03T06:17:42.601121Z

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=2026-06-27T12:42:08.487008Z digest=sha256:39ba4a4eb5ce9801cfd7ba4d4f32237d762a921e31c95416976924dc0ec411af

Observation 749b8657-68fe-4a0e-9624-b96f51437f99 · inbound

Unifying Local Communications and Local Updates for LLM Pretraining cites this paper.

Unifying Local Communications and Local Updates for LLM Pretraining Local SGD Converges Fast and Communicates Little

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-07-03T04:07:37.142464Z

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=2026-06-27T14:07:42.062805Z digest=sha256:233b115875b90965d3e877e4158a49ba433d12ed3dc620eaab82c91435cca18e

Observation 08b876d8-2cf1-4467-b631-bf9bf77a708b · inbound

Sensing-Native Over-the-Air Federated Learning cites this paper.

Sensing-Native Over-the-Air Federated Learning Local SGD Converges Fast and Communicates Little

Reference 24

Resolution
verified exact
local_arxiv, observed 2026-07-03T18:28:48.834443Z

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=2026-06-27T03:05:38.176119Z digest=sha256:4e640e34480a88914d10f634a526b8f7849e029574f5da00680ecc2063025801

Observation 7a06bcfd-7e34-4d99-91c8-3d5019906aef · inbound

What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity cites this paper.

What's in a Smoothness Constant? Tighter Rates for Local SGD with Bounded Second-order Heterogeneity Local SGD Converges Fast and Communicates Little

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-02T01:23:31.636978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:23:31.636978Z digest=sha256:1cf1daddc2c55272506e3067582a93467c84437e8875ae8ef576bd2846a4f5ab

Observation 16251647-6b98-4eff-8aa0-8e5b04ba44a1 · inbound

Controlled Periodic Synchronization for Efficient Data-Parallel Training cites this paper.

Controlled Periodic Synchronization for Efficient Data-Parallel Training Local SGD Converges Fast and Communicates Little

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-01T08:10:49.414993Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-01T08:10:49.414993Z digest=sha256:68ad213b95d1003dbb8183cdbbea9b35e60dc168773a2d4791957d7c610742cc