Paper Citation Record · LEDGER

Federated Continual Learning for Edge-AI: A Comprehensive Survey

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.13740.

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

pith.paper-citation-record.v1
2411.13740 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T13:56:53.304013Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T03:57:38.780121Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation cac41142-7a4d-4d4b-a2c4-954f275a8fb4 · inbound

Federated Continual Learning: Concepts, Challenges, and Solutions cites this paper.

Federated Continual Learning: Concepts, Challenges, and Solutions Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-08T13:56:53.304013Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33a30714-9a21-48b6-b759-4d4fa4d31336 · inbound

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning cites this paper.

Task-agnostic Low-rank Residual Adaptation for Efficient Federated Continual Fine-Tuning Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 13

Resolution: verified exact; arxiv_id, observed 2026-05-22T13:54:52.922620Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 6d7f5052-90ed-4c80-a2ba-b7f05c86ec62 · inbound

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning cites this paper.

FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learning Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-04T22:29:14.870917Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1186940d-22d8-4948-a6df-5447980c2b7f · inbound

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks cites this paper.

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-20T11:58:15.356683Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 243f51b8-3e58-4665-b40d-58dc335f18b9 · inbound

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks cites this paper.

PMF-CL: Pareto-Minimal-Forgetting Continual Learner for Conflicting Tasks Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T18:14:59.802816Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 656cb164-7110-4701-af9b-879523dfd30a · inbound

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data cites this paper.

Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data Federated Continual Learning for Edge-AI: A Comprehensive Survey

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-07-03T03:57:38.781598Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.