Paper Citation Record · LEDGER

FedML: A Research Library and Benchmark for Federated Machine Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 18 inbound Pith citation observations for arXiv:2007.13518.

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

pith.paper-citation-record.v1
2007.13518 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 18 of 18 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 18 of 18 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:25:38.318364Z

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

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

358
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation ad379339-5272-4a48-90ca-fb13f8d13da1 · inbound

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

Distributionally Robust Federated Learning with Client Drift Minimization FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T15:25:38.318364Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e33bc840-8b29-46ee-88df-29c10d82c341 · inbound

ByzFL: Research Framework for Robust Federated Learning cites this paper.

ByzFL: Research Framework for Robust Federated Learning FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-07T12:15:44.947535Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2399fafd-f9c5-409e-a3a1-414decc37c37 · inbound

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark cites this paper.

HtFLlib: A Comprehensive Heterogeneous Federated Learning Library and Benchmark FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T10:55:14.924426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4aa12c11-97df-4765-b8cc-d155400e6bee · inbound

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models cites this paper.

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T05:03:01.441096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd185860-95d3-4693-97eb-9c0c38cf92c5 · inbound

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources cites this paper.

Flotilla: A scalable, modular and resilient federated learning framework for heterogeneous resources FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T20:37:13.373112Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7108ee1c-518a-499f-b670-e0e6807818fe · inbound

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning cites this paper.

FLsim: A Modular and Library-Agnostic Simulation Framework for Federated Learning FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T17:13:27.595839Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebae7ffa-3f3b-4c4f-b74f-3b50ef12f541 · inbound

PyG 2.0: Scalable Learning on Real World Graphs cites this paper.

PyG 2.0: Scalable Learning on Real World Graphs FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T15:03:07.496193Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c93aa297-ec1e-433b-82b3-2369fb561e9b · inbound

Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces cites this paper.

Benchmarking Robust Aggregation in Decentralized Gradient Marketplaces FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T05:02:47.816288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation db8d14bb-c852-497c-8df0-82d6da36ae23 · inbound

Understanding Communication Backends in Cross-Silo Federated Learning cites this paper.

Understanding Communication Backends in Cross-Silo Federated Learning FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-11T11:16:08.455863Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 7b7bd564-4cf3-4a8e-bd54-e581211f769a · inbound

HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus cites this paper.

HadAgent: Harness-Aware Decentralized Agentic AI Serving with Proof-of-Inference Blockchain Consensus FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-10T12:20:22.542420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a427774e-a6bf-44b8-92fe-b318ae1f94f0 · inbound

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy cites this paper.

Enhanced Privacy and Communication Efficiency in Non-IID Federated Learning with Adaptive Quantization and Differential Privacy FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:41:09.388850Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ebb5f276-337b-493b-8757-b62b5aa91e0e · inbound

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data cites this paper.

Sample Selection Using Multi-Task Autoencoders in Federated Learning with Non-IID Data FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-09T01:49:33.954731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 49e016e2-4ecf-4bd3-ab03-9ce763100390 · inbound

Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers cites this paper.

Clipping Makes Distributed and Federated Asynchronous SGD Robust to Stragglers FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 104

Resolution: verified exact; arxiv_id, observed 2026-07-03T13:58:21.760421Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation e37e9187-a883-46c9-913a-5152329f85d4 · inbound

Decentralised AI Training and Inference with BlockTrain cites this paper.

Decentralised AI Training and Inference with BlockTrain FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:40:00.423897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ec9a9fa0-f6a3-4e81-9a2b-426109a53c66 · inbound

Decentralised AI Training and Inference with BlockTrain cites this paper.

Decentralised AI Training and Inference with BlockTrain FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-12T12:29:35.403453Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11540710-0762-484c-a5cd-9461e2ad9590 · inbound

Robust Federated Learning Under Real-World Client Churn cites this paper.

Robust Federated Learning Under Real-World Client Churn FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-07-09T22:36:36.197323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a857fa49-0abd-4667-be37-7d24020e0304 · inbound

Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub cites this paper.

Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-01T12:15:06.221944Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ff03702-00fa-4fdf-8d40-345febde270d · inbound

FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation cites this paper.

FraQ: Efficient Coordinate-Space Recompression for Federated Low-Rank Adaptation FedML: A Research Library and Benchmark for Federated Machine Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T16:11:58.785768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.