Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-06T13:39:45.935117Z
Paper Citation Record · LEDGER
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems
As of 19 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2507.20444.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact1
- verified fuzzy13
- unresolved1
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 47fe6885-12e5-4946-9cf1-8da37430799b · outbound
This paper cites A taxonomy of ai techniques for 6g communication networks.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems A taxonomy of ai techniques for 6g communication networks
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:51.025779Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c81bba32-dd57-4c44-8895-f5f26a066d31 · outbound
This paper cites Federated learning meets blockchain in edge computing: Opportunities and challenges.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Federated learning meets blockchain in edge computing: Opportunities and challenges
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:50.755419Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fe4053de-745c-456e-8811-944b2b0cc025 · outbound
This paper cites Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions
Reference 3
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T13:39:46.565579Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bccc884d-4fb8-4048-8d33-e5c46e77f404 · outbound
This paper cites Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Reinforcement Learning With LLMs Interaction For Distributed Diffusion Model Services
Reference 4
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T13:39:44.326967Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d20a22b9-05dd-4057-9935-21b28e9889dd · outbound
This paper cites Shift to 6g: Exploration on trends, vision, requirements, technologies, research, and standardization efforts.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Shift to 6g: Exploration on trends, vision, requirements, technologies, research, and standardization efforts
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:50.334741Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a58465e3-c276-469a-b1f3-fc07a6b52d02 · outbound
This paper cites A vision on the artificial intelligence for 6g communica- tion.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems A vision on the artificial intelligence for 6g communica- tion
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:50.014778Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3b0d8909-575e-4b2c-b00d-085232663c51 · outbound
This paper cites Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:49.754772Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6a70360e-3caa-4d1c-9fb7-19702dba9a01 · outbound
This paper cites Towards 6g internet of things: Recent advances, use cases, and open challenges.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Towards 6g internet of things: Recent advances, use cases, and open challenges
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:49.399267Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2754cab5-a37d-4d7e-ac8f-1ccf8680b39d · outbound
This paper cites Emerging technologies for 6g communication networks: Machine learning approaches.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Emerging technologies for 6g communication networks: Machine learning approaches
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:48.974752Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation aa022013-2e65-48f3-a8a7-b20712f689cc · outbound
This paper cites Artificial intelligence applications and self-learning 6g networks for smart cities digital ecosystems: Taxonomy, challenges, and future directions.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Artificial intelligence applications and self-learning 6g networks for smart cities digital ecosystems: Taxonomy, challenges, and future directions
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:48.615816Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 3d59945d-63a2-4219-91fe-2065c0e22933 · outbound
This paper cites Out-of-distribution detection- assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Out-of-distribution detection- assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:48.184759Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 66da9720-5808-4962-a97b-90aac67e4064 · outbound
This paper cites Similarity-measured isolation forest: Anomaly detection method for machine monitoring data.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Similarity-measured isolation forest: Anomaly detection method for machine monitoring data
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:47.885066Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6e6f735b-886d-48e5-86c8-477e215420da · outbound
This paper cites Anomaly detection of vehicle data based on lof algorithm.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Anomaly detection of vehicle data based on lof algorithm
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:47.506777Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ef1db013-70b9-40b4-baf4-a7bd584d61f7 · outbound
This paper cites Fl-mgvn: Federated learning for anomaly detection using mixed gaussian variational self-encoding network.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Fl-mgvn: Federated learning for anomaly detection using mixed gaussian variational self-encoding network
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:47.215989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation bf5f7a1b-63cf-4739-ad47-abb051b6f9f9 · outbound
This paper cites Dïot: A federated self-learning anomaly detec- tion system for iot.
Enhancing QoS in Edge Computing through Federated Layering Techniques: A Pathway to Resilient AI Lifelong Learning Systems Dïot: A federated self-learning anomaly detec- tion system for iot
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-06T13:39:46.945804Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.