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Paper Citation Record · LEDGER

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications

As of 22 August 2026, this Paper Citation Record lists 1 of 1 outbound references and 1 inbound Pith citation observation for arXiv:2508.08479.

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

pith.paper-citation-record.v1
2508.08479 v1

Coverage vector

measured 1 of 1 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:34:10.608689Z

measured 2 of 2 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 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:37:18.926788Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T21:37:19.042894Z

Reference resolution

1 of 1 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved1
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7779cbd7-3c75-4579-bad3-93ca36c73723 · outbound

This paper cites an unresolved cited work.

Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications Unresolved cited work

Reference 1

Resolution
unresolved
no resolver link, observed 2026-08-05T21:34:10.608689Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T21:34:10.608689Z digest=sha256:c7f68691afab21990abce5f34045530ce3e6e45ed955fb6392eb0aaa77e40d5c

Pith citing papers

Observation b7523af5-a194-4496-b9db-cf31a05a3fdb · inbound

Investigation of Electromagnetic and Muonic Air-Shower Components using IceTop Simulations cites this paper.

Investigation of Electromagnetic and Muonic Air-Shower Components using IceTop Simulations Benchmarking Federated Learning for Throughput Prediction in 5G Live Streaming Applications

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-05T21:37:19.104684Z

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.

source=pdf_text observed=2026-08-05T21:37:18.926788Z digest=sha256:11f9d711788efbea42537a4fcd9ca04524a1ec4077ea56a8e9289e621fd73d75