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Paper Citation Record · LEDGER

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 24 inbound Pith citation observations for arXiv:2008.03606.

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

pith.paper-citation-record.v1
2008.03606 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 24 of 24 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:44:36.995604Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T17:48:46.106003Z

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Outbound references

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Pith citing papers

Observation a36a2f95-b566-41f6-ac08-d05325075699 · inbound

Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition cites this paper.

Towards Adaptive Asynchronous Federated Learning for Human Activity Recognition Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 10

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Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T15:37:46.988876Z digest=sha256:63b93f227e1d79635e79f3077fbf9a6fcc8c03c595b8ce978d0ec434eb9ce94c

Observation 91fc2058-24cd-4155-82c9-20ec3ccb5f7f · inbound

GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning cites this paper.

GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 30

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source=pdf_text observed=2026-08-11T22:06:13.636404Z digest=sha256:cb834a760a416d2a31bee36fea7a6785e318c625cbb0ea5cf6c4fecc9372e74a

Observation e0fcecd2-c2f0-4b04-8357-245811949449 · inbound

Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning cites this paper.

Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 10

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no resolver link, observed 2026-08-11T14:49:21.375923Z

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source=arxiv_source observed=2026-08-11T14:49:21.375923Z digest=sha256:8ddb296018bfb7c733c9acd5a56c0059c395c0e881a20bfd5a140499a2161656

Observation 786dd87f-2446-4ce6-9562-af1a5669532f · inbound

Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning cites this paper.

Federated Testing (FedTest): A New Scheme to Enhance Convergence and Mitigate Adversarial Attacks in Federating Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 6

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source=pdf_text observed=2026-08-10T18:38:28.695348Z digest=sha256:8a3c1044b251de6eb9b1707086c9e071e790153396601c0df49e84c5120f8de0

Observation a8c79c92-353d-472d-b395-01bde54f3caf · inbound

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks cites this paper.

Communication-Efficient Federated Learning by Quantized Variance Reduction for Heterogeneous Wireless Edge Networks Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 38

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source=pdf_text observed=2026-08-10T18:35:01.184510Z digest=sha256:817525e6ed751f15d64df42312264d03a04e8e9649d1196de89ff14da635d1b3

Observation 949c4b7c-945e-4181-93d7-6f5bbb66c910 · inbound

Interaction-Aware Gaussian Weighting for Clustered Federated Learning cites this paper.

Interaction-Aware Gaussian Weighting for Clustered Federated Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 1942

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no resolver link, observed 2026-08-09T05:10:22.195022Z

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source=pdf_text observed=2026-08-09T05:10:22.195022Z digest=sha256:7383ee7a6c6388bc8c506c60068a14f5ac609fed75395f9513491d2b65c6eb23

Observation 508eb73e-2bc2-47fa-87f1-34c61b29b067 · inbound

Non-convex composite federated learning with heterogeneous data cites this paper.

Non-convex composite federated learning with heterogeneous data Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 12

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source=pdf_text observed=2026-08-09T00:13:44.474773Z digest=sha256:5ebcac0a955bad5d14917905567637bc09e1cb4ce407701c7aa2133799b81548

Observation 18b85d2e-b026-49c4-b0d9-86e5c8d14a2f · inbound

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction cites this paper.

TACO: Tackling Over-correction in Federated Learning with Tailored Adaptive Correction Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 40

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no resolver link, observed 2026-08-16T10:44:36.995604Z

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source=pdf_text observed=2026-08-16T10:44:36.995604Z digest=sha256:2fe50ae4286b86eaf0f77157608e8a41ba71195f2a645be60e2c7008a93d584b

Observation 4d2bfdef-2419-4422-b68a-d063cf0d038c · inbound

FedDuA: Doubly Adaptive Federated Learning cites this paper.

FedDuA: Doubly Adaptive Federated Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 4

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source=pdf_text observed=2026-08-15T21:08:31.755348Z digest=sha256:06e20427cbeb10c6cc40a91104dbdafa07add2be6614eeab904f78b6aec47c96

Observation e4c13a73-fcb5-43cf-bba9-dc825317a819 · inbound

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions cites this paper.

Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitions Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 6

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source=pdf_text observed=2026-08-07T11:19:03.261637Z digest=sha256:9186f4940e4f540be65e9ea4e5acf1263857fd8a4db483abbb0d26d9fbe726ac

Observation 9a393af2-9eff-4251-a62e-8943c88dac49 · 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 Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 71

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no resolver link, observed 2026-08-06T21:27:24.682601Z

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source=pdf_text observed=2026-08-06T21:27:24.682601Z digest=sha256:5d0997d7c99e613625f6e5bf8c359c8c4cc4e836fd00e94c27756b39de9e0249

Observation 7d524f13-ccaa-4045-85f0-25d78fe3140b · inbound

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios cites this paper.

FedWCM: Unleashing the Potential of Momentum-based Federated Learning in Long-Tailed Scenarios Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 25

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source=pdf_text observed=2026-08-06T15:49:06.455247Z digest=sha256:76ba64fcf13187ce5d041a0c27c38ef4b8556b09a8d25982f761cfb4732bea7c

Observation b3ca87f9-737d-4792-b967-c0ce0a1568de · 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 Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 18

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source=arxiv_source observed=2026-08-06T11:24:43.227348Z digest=sha256:6c355a93b9bd03bddd396a04bbb85726312d370cbae7f6ceb1db0ee6b38aa255

Observation fe769f28-10b0-4585-b1af-54cddb612445 · inbound

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach cites this paper.

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 40

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source=pdf_text observed=2026-08-05T22:15:40.941871Z digest=sha256:07ba3c189f97590dfddf27bd089ec9cdc8f81c07b97c65e2c17b5f41e0d2d979

Observation 8cb534a3-488e-4bd9-8879-46b1821e8d09 · inbound

Generalizable Federated Learning using Client Adaptive Focal Modulation cites this paper.

Generalizable Federated Learning using Client Adaptive Focal Modulation Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 28

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source=arxiv_source observed=2026-08-05T20:19:05.655015Z digest=sha256:e6d82c4a0e7160220eb7039575d8efe4f7073a071a6842313bd0867ca375c120

Observation 35b018fc-3de1-4c02-9112-5fa910202634 · inbound

Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation cites this paper.

Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 42

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source=pdf_text observed=2026-08-05T11:23:48.769342Z digest=sha256:143e86eaccabbcb359c0d9e10f536596f43f429043dd4c05a78c6f39856655a0

Observation 94baa0a9-a528-4eb6-97cd-2e02129a3ef8 · 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 Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 113

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source=arxiv_source observed=2026-08-04T21:06:26.246648Z digest=sha256:15b83a16841dc31a5a3b9b3ca08bcae37e0b030c5d92f44799adccf605d0fbd3

Observation 2975085c-548d-4197-afaf-9fd569ada01d · inbound

Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning cites this paper.

Divergence-Based Adaptive Aggregation for Byzantine Robust Federated Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 15

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source=pdf_text observed=2026-08-03T11:20:09.214747Z digest=sha256:32198a1d028f39030cadb248973427ceb2f8a52423bd6ab3823da02231535c19

Observation c2da43d1-741c-43bd-9277-5e88d51f9eb3 · inbound

LoRDO: Distributed Low-Rank Optimization with Infrequent Communication cites this paper.

LoRDO: Distributed Low-Rank Optimization with Infrequent Communication Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 11

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source=pdf_text observed=2026-08-03T04:44:55.507006Z digest=sha256:7c4041f945b09ff9a6c211922eccab318fcff64fa49f17480977427b614cb670

Observation 5a33a09f-7a04-4c55-9103-01a1da9cc2b6 · inbound

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning cites this paper.

FedBCD:Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 18

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arxiv_id, observed 2026-05-15T16:00:10.283043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-05-15T15:58:41.532419Z digest=sha256:7120cac5eacbd7bc4d55c88384017539467ca3a401254ddef2913a34c8e25697

Observation ef18d8b4-34fc-41d4-9fb2-87c525a90f4e · inbound

Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning cites this paper.

Local MixVR: Breaking the Communication-Sample Dependence in Distributed Learning Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 7

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arxiv_id, observed 2026-07-01T21:16:14.066799Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-28T17:22:36.503465Z digest=sha256:d2c1ff2d0614fbb287fc1c5f169da4bd80b64ee6684bd028eed68afa16880fa7

Observation 5b146191-541e-4f26-9b94-e0e9a0bda9c5 · inbound

SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums cites this paper.

SILAGE: Memory-Efficient, Full-Gradient-Free Nonconvex Optimization for Nested Finite Sums Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 17

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arxiv_id, observed 2026-07-03T17:48:46.107866Z

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No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

source=pdf_text observed=2026-06-27T03:42:57.288700Z digest=sha256:8a1e391ed538aaa9a780fe9b31c1c22c1870a52f7979979437cf7eb0a7a70208

Observation 7f2fca91-9904-4dc7-a8a9-043fee56bbbc · inbound

FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity cites this paper.

FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneity Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 98

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source=arxiv_source observed=2026-07-12T00:07:55.485589Z digest=sha256:5de31001d5582727abc41fc357765f663f5410b12f217b3d6d144a14ee8afeae

Observation 0e4881f0-f920-4e79-adce-d56fb15177c2 · 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 Mime: Mimicking Centralized Stochastic Algorithms in Federated Learning

Reference 22

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