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Paper Citation Record · LEDGER

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications

As of 8 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 0 inbound Pith citation observations for arXiv:2506.09090.

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

pith.paper-citation-record.v1
2506.09090 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:09:22.276851Z

measured 9 of 9 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 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

9 of 9 outbound references displayed

  • verified exact0
  • verified fuzzy6
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation d0e80e4d-4181-4495-a264-619e3a3c7358 · outbound

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

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Communication - Efficient Learning of Deep Networks from Decentralized Data,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:23.661071Z

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.

source=pdf_text observed=2026-08-07T05:09:21.103634Z digest=sha256:7ff698af91a8d2211527b474f3888e74e483ae2fa93a632e5b15f5b5a0619929

Observation 79c87124-c66a-45e8-921a-852837ff9700 · outbound

This paper cites A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting,

Reference 2

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:23.479645Z

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.

source=pdf_text observed=2026-08-07T05:09:21.173512Z digest=sha256:d362cfcfbbb982afec0e102fa45cfe8484819f4e468a1308770499c6bdafbf29

Observation 52dc1f47-a3cd-42f0-b341-ef26fc164785 · outbound

This paper cites Asynchronous Federated Optimization.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Asynchronous Federated Optimization

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-07T05:09:21.380178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:09:21.380178Z digest=sha256:413c4d6290888e28bf0a43143d9d8953e8f6890857f012eed939c04b11b2e651

Observation 8c2b76dc-0bba-47b7-a397-74438116a82e · outbound

This paper cites Adaptive Communication for Scalable Distributed AdaBoost,.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Adaptive Communication for Scalable Distributed AdaBoost,

Reference 4

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:23.309129Z

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.

source=pdf_text observed=2026-08-07T05:09:21.545425Z digest=sha256:fddf12b9c01365d7ed968f0cdf69d9fbc3d99cfbec48033ada889fc29b89dcb8

Observation cdbbd396-b03d-458a-a761-7eb34dacb10f · outbound

This paper cites Federated Learning for Mobile Keyboard Prediction.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Federated Learning for Mobile Keyboard Prediction

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-07T05:09:21.648447Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:09:21.648447Z digest=sha256:60aa7a00702586ded74818e08a570929f6776516a897e872b95ab60dbfc14528

Observation ac07a6cf-a8bf-4075-8108-f953e8412c86 · outbound

This paper cites Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning,.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:23.083392Z

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.

source=pdf_text observed=2026-08-07T05:09:21.813676Z digest=sha256:d96aee9a4ed705c307179cd5abb7b6c5865848addf546d7c0c83d6ab74a9e98d

Observation 41f625db-9629-4ff1-978d-46f2521bd1c7 · outbound

This paper cites Multi -Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation,.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Multi -Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:22.922188Z

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.

source=pdf_text observed=2026-08-07T05:09:22.025613Z digest=sha256:370648d18bf90ac7da6811866df421daf858ac71f48ce06f01cff6b3658071bf

Observation 5bcb93b9-ee52-41d8-b27e-f7f15b2f80f9 · outbound

This paper cites DÏoT: A Federated Self -learning Anomaly Detection Sy stem for IoT,.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications DÏoT: A Federated Self -learning Anomaly Detection Sy stem for IoT,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-07T05:09:22.695097Z

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.

source=pdf_text observed=2026-08-07T05:09:22.134500Z digest=sha256:e5afa4c5f5568fc09b7f2c3c4fc3b6efe65369f4895325dc76e1a02348a1fb5c

Observation bb4952e9-fe83-4f5a-b7a4-0549adf78fa6 · outbound

This paper cites Advances and Open Problems in Federated Learning.

Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications Advances and Open Problems in Federated Learning

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T05:09:22.276851Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T05:09:22.276851Z digest=sha256:c0cb25d3f6d24edec729c60f740b3b5a5673439b9f390d4700599f06687b66e3

Pith citing papers

No inbound Pith citation observations are available.