Paper Citation Record · LEDGER

FedHQ: Hybrid Runtime Quantization for Federated Learning

As of 22 August 2026, this Paper Citation Record lists 16 of 16 outbound references and 2 inbound Pith citation observations for arXiv:2505.11982.

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

pith.paper-citation-record.v1
2505.11982 v1

Coverage vector

measured 16 of 16 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:49:42.583867Z

measured 18 of 18 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-20T14:11:53.371521Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T14:13:21.240043Z

Reference resolution

16 of 16 outbound references displayed

verified exact1
verified fuzzy4
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 16914665-bbdc-47ff-8509-4d3dce0cba0a · outbound

This paper cites an unresolved cited work.

FedHQ: Hybrid Runtime Quantization for Federated Learning Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-15T20:49:42.905343Z

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.

Observation 61046b70-6f05-4943-9486-d3a767e9dccc · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

FedHQ: Hybrid Runtime Quantization for Federated Learning GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.519555Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e144fd4b-52f1-420d-8725-028e6ea1b030 · outbound

This paper cites T., Moreno, I.

FedHQ: Hybrid Runtime Quantization for Federated Learning T., Moreno, I

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:49:42.889818Z

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.

Observation 79370670-fb47-490d-94ad-3dfc00d6f992 · outbound

This paper cites and Yonetani, R.

FedHQ: Hybrid Runtime Quantization for Federated Learning and Yonetani, R

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:49:42.875710Z

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.

Observation bf7b15a8-6780-4163-b81f-e2a03bfd1b68 · outbound

This paper cites Post- training quantization on diffusion models.

FedHQ: Hybrid Runtime Quantization for Federated Learning Post- training quantization on diffusion models

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:49:42.861627Z

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.

Observation 5062fed6-9486-4867-8e9b-ec8d6e3479e0 · outbound

This paper cites Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point.

FedHQ: Hybrid Runtime Quantization for Federated Learning Towards Federated Learning with On-device Training and Communication in 8-bit Floating Point

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-08-15T20:49:42.658075Z

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.

Observation a6329675-4f1e-4c9c-bc74-1030462a20e5 · outbound

This paper cites Equality Saturation for Tensor Graph Superoptimization.

FedHQ: Hybrid Runtime Quantization for Federated Learning Equality Saturation for Tensor Graph Superoptimization

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.573808Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 953e4cfd-1355-41f1-813d-19b446779624 · outbound

This paper cites Federated Learning with Non-IID Data.

FedHQ: Hybrid Runtime Quantization for Federated Learning Federated Learning with Non-IID Data

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.579309Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 489e08a5-8667-480f-899f-cbd2c4cd0c96 · outbound

This paper cites FedFQ: Federated Learning with Fine-Grained Quantization.

FedHQ: Hybrid Runtime Quantization for Federated Learning FedFQ: Federated Learning with Fine-Grained Quantization

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.534162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 17cb35b1-5672-4e21-b632-8b63a7a959e7 · outbound

This paper cites Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks.

FedHQ: Hybrid Runtime Quantization for Federated Learning Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.529606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab66a970-6cef-4203-ab91-e57a881a02fb · outbound

This paper cites Communication-Efficient Learning of Deep Networks from Decentralized Data.

FedHQ: Hybrid Runtime Quantization for Federated Learning Communication-Efficient Learning of Deep Networks from Decentralized Data

Reference 2017

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.544312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ce2b51b0-a1b3-46d8-9d7c-8ac1c0fd044d · outbound

This paper cites Deep reinforcement learning-based quantization for federated learning.

FedHQ: Hybrid Runtime Quantization for Federated Learning Deep reinforcement learning-based quantization for federated learning

Reference 2018

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T20:49:42.847365Z

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.

Observation 7338d5ec-0be7-496d-acca-3644481367da · outbound

This paper cites LLM-QAT: Data-Free Quantization Aware Training for Large Language Models.

FedHQ: Hybrid Runtime Quantization for Federated Learning LLM-QAT: Data-Free Quantization Aware Training for Large Language Models

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.539574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4578567a-2801-4263-bc9b-4e4404fcef61 · outbound

This paper cites A survey of low-bit large language models: Basics, systems, and algorithms.

FedHQ: Hybrid Runtime Quantization for Federated Learning A survey of low-bit large language models: Basics, systems, and algorithms

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.524626Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1308f5d8-a215-4f97-b5b1-837836e54aba · outbound

This paper cites FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization.

FedHQ: Hybrid Runtime Quantization for Federated Learning FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.558707Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7dc2bb55-f0f9-4c15-92e5-db165e99de6f · outbound

This paper cites EfficientQAT: Efficient Quantization-Aware Training for Large Language Models.

FedHQ: Hybrid Runtime Quantization for Federated Learning EfficientQAT: Efficient Quantization-Aware Training for Large Language Models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T20:49:42.514521Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 6166a7d4-5b10-44e8-a5cb-e3b46fc3b2c5 · inbound

Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance cites this paper.

Quantization Impact on the Accuracy and Communication Efficiency Trade-off in Federated Learning for Aerospace Predictive Maintenance FedHQ: Hybrid Runtime Quantization for Federated Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-11T05:26:02.225987Z

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.

Observation c550fd70-0fd7-4a61-9d25-d7a0bca97463 · 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 FedHQ: Hybrid Runtime Quantization for Federated Learning

Reference 36

Resolution: malformed identifier; arxiv_id, observed 2026-05-20T14:13:21.241653Z

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.