Paper Citation Record · LEDGER

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning

As of 11 August 2026, this Paper Citation Record lists 12 of 12 outbound references and 0 inbound Pith citation observations for arXiv:2608.07157.

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

pith.paper-citation-record.v1
2608.07157 v1

Coverage vector

measured 12 of 12 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:52:51.863127Z

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+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

12 of 12 outbound references displayed

verified exact3
verified fuzzy5
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bb17382f-f8f7-4898-b3e5-f755aba37b0a · outbound

This paper cites Exploiting shared representations for personalized federated learning,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Exploiting shared representations for personalized federated learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:52:52.198574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 324f1ff0-8ee8-464b-9de5-1daa235259a2 · outbound

This paper cites On the convergence and stability of distributed sub- model training,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning On the convergence and stability of distributed sub- model training,

Reference 8

Resolution: verified exact; raw_fallback, observed 2026-08-10T13:52:52.074883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 5608a2dc-91c2-43a0-a8d3-9e7363723c5d · outbound

This paper cites Communication-efficient federated learning via knowledge distillation,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Communication-efficient federated learning via knowledge distillation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:52:52.137147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 418b3bd5-d223-4dd0-90db-65031cc5b2cd · outbound

This paper cites Client Selection in Federated Learning based on Gradients Importance.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Client Selection in Federated Learning based on Gradients Importance

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:52:51.907226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f72fc6d9-f828-4b89-bfac-1d4eba9c1825 · outbound

This paper cites Adaptive Personalized Federated Learning.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Adaptive Personalized Federated Learning

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-10T13:52:51.837144Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b339efaf-8d44-4b50-9738-90f8e759df1b · outbound

This paper cites Sparse communication for distributed gradient descent,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Sparse communication for distributed gradient descent,

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:52:52.152982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 66c9efb7-8032-4a4c-b50b-3e48f217bd6a · outbound

This paper cites Federated Learning for Mobile Keyboard Prediction.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Federated Learning for Mobile Keyboard Prediction

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-10T13:52:51.804155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4620ed8a-f24e-469d-8bb6-376bef9dc983 · outbound

This paper cites LEAF: A Benchmark for Federated Settings.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning LEAF: A Benchmark for Federated Settings

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-10T13:52:51.809854Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad77f6fc-60ef-424a-9b8a-720db0c19f5e · outbound

This paper cites Distributed Learning of Deep Neural Networks using Independent Subnet Training.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Distributed Learning of Deep Neural Networks using Independent Subnet Training

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-10T13:52:51.851584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e917d1b7-be08-43f7-a191-02060cef1ca2 · outbound

This paper cites FedCP: Separating feature information for personalized federated learning via conditional policy,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning FedCP: Separating feature information for personalized federated learning via conditional policy,

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:52:52.168697Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 4b884a34-73eb-43d0-a0dc-70355a110d5c · outbound

This paper cites Learnable Sparse Customization in Heterogeneous Edge Computing.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Learnable Sparse Customization in Heterogeneous Edge Computing

Reference 2024

Resolution: verified exact; local_arxiv, observed 2026-08-10T13:52:51.929235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation be3ad9a9-4c37-4c6f-9a6b-1e7fcf36321d · outbound

This paper cites Efficient personalized federated learning via sparse model-adaptation,.

Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learning Efficient personalized federated learning via sparse model-adaptation,

Reference 2026

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T13:52:52.183661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Pith citing papers

No inbound Pith citation observations are available.