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Paper Citation Record · LEDGER

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients

As of 20 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2509.03503.

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

pith.paper-citation-record.v1
2509.03503 v1

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T10:59:57.163858Z

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

13 of 13 outbound references displayed

  • verified exact2
  • verified fuzzy3
  • unresolved6
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 553782f1-621e-4dc0-9aab-f1cddcae824e · outbound

This paper cites Optimization without Backpropagation.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Optimization without Backpropagation

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:56.170025Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:56.170025Z digest=sha256:3fae354794e884fd019d21e90a871254bce305b6906ca5cbcd82e2ef027f54fb

Observation b6cba81b-ae3e-47d7-9a37-b44f895ca68a · outbound

This paper cites Cristiano Gratton, Naveen K.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Cristiano Gratton, Naveen K

Reference 4

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T10:59:57.881652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.363742Z digest=sha256:02beae61fb6c81741ea83d19483de0146ad633f8c4b954895a844603ecaa7730

Observation 634e1e70-bf53-42bd-a622-5bf6ff4daa37 · outbound

This paper cites Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun

Reference 5

Resolution
metadata mismatch
raw_fallback, observed 2026-08-05T10:59:57.642043Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.423984Z digest=sha256:13cbd1059129d9d4fd533a29797b1f56bd3e7dc16ac11bee2587349e6a75acdb

Observation 560da3f1-4c5b-400f-8ddb-f3a99df74fe2 · outbound

This paper cites Critical Learning Periods in Federated Learning.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Critical Learning Periods in Federated Learning

Reference 10

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:59:57.405315Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.843424Z digest=sha256:aaf190dfc743b9cc7b262c6535cd49e2aa09e429205b4a7a47668b401dba1548

Observation 92ca0ac0-8cfd-4018-a0f4-ad66b029595f · outbound

This paper cites Federated Learning with Non-IID Data.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Federated Learning with Non-IID Data

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:56.953435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:56.953435Z digest=sha256:89107845fd544315afebcc07464d1e0e7c958e999f0caae9c8423cd25a2c9732

Observation 059722c0-de5f-48b2-8dfb-f0575400e967 · outbound

This paper cites The Rademacher distribution exhibits considerably lower variance and better overall accuracy than the Gaussian distribution.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients The Rademacher distribution exhibits considerably lower variance and better overall accuracy than the Gaussian distribution

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:59:58.252085Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:57.163858Z digest=sha256:b9d99c9248e9c49484f4bb49dd087ce29ba162bbd9e4b0d3b758a4ca077170da

Observation c06244c9-ddd9-4c71-97ba-b06d0d178b00 · outbound

This paper cites A Vaswani.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients A Vaswani

Reference 1992

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:56.741954Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:56.741954Z digest=sha256:24196cd59e7565f7c6c406ab46ec262136416760e7443eb8404899418e9f59ae

Observation 308c911d-e79b-4961-ae02-36b73d28eb36 · outbound

This paper cites cs.toronto.edu/˜kriz/learning-features-2009-TR.pdf.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients cs.toronto.edu/˜kriz/learning-features-2009-TR.pdf

Reference 2009

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:59:58.567079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.502075Z digest=sha256:01c8ee1d27180dd4275cae583e2d4180a9a467ea72e7de59514fa1077835125b

Observation ff20ec9b-7256-4624-8176-23ad8325b2fe · outbound

This paper cites A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients A Survey on Model-heterogeneous Federated Learning: Problems, Methods, and Prospects

Reference 2015

Resolution
verified exact
local_arxiv, observed 2026-08-05T10:59:58.065420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.297277Z digest=sha256:2a1523841b22cb40379024e196e052fde0f11b60f317915bd4b9107787270791

Observation 0158103e-9795-4759-885f-5fbc015ae4a9 · outbound

This paper cites Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Understanding Why ViT Trains Badly on Small Datasets: An Intuitive Perspective

Reference 2018

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:57.055832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:57.055832Z digest=sha256:7cfcce3aac20d67937facda69c2958702856e885007751d541a1eb21cba3f70c

Observation ded39750-3699-413d-942e-3d6148f3e185 · outbound

This paper cites Zhong Long, Yuling Chen, Hui Dou, Yun Luo, Chaoyue Tan, and Yancheng Sun.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients Zhong Long, Yuling Chen, Hui Dou, Yun Luo, Chaoyue Tan, and Yancheng Sun

Reference 2020

Resolution
verified fuzzy
raw_fallback, observed 2026-08-05T10:59:58.409231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-08-05T10:59:56.579770Z digest=sha256:0d712ce49e6a2a54e44c2177d5a5b7fc922d2fcf68b703dcf2cff855b9c32b32

Observation 69195c1a-e38f-43ff-8b31-9b081e54dad6 · outbound

This paper cites A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets

Reference 2022

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:56.235507Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:56.235507Z digest=sha256:6609261661d6742ab75a49cecef01dafc6c019a58b5474441fbf084130de35d6

Observation 1424e056-fe05-4ebf-8095-88f9e974123e · outbound

This paper cites ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity.

Warming Up for Zeroth-Order Federated Pre-Training with Low Resource Clients ZeroFL: Efficient On-Device Training for Federated Learning with Local Sparsity

Reference 2024

Resolution
unresolved
no resolver link, observed 2026-08-05T10:59:56.662419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T10:59:56.662419Z digest=sha256:0cf68bdb4daa454b832ba03fd6071cb6a0374052313056607009cfbde7018be3

Pith citing papers

No inbound Pith citation observations are available.