Paper Citation Record · LEDGER

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT

As of 17 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2412.03950.

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

pith.paper-citation-record.v1
2412.03950 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:57:48.843442Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

25 of 25 outbound references displayed

verified exact1
verified fuzzy20
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40067642-dec4-45eb-844e-29cff8dbd164 · outbound

This paper cites Federated learning on non-IID data: A survey,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Federated learning on non-IID data: A survey,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-11T21:57:48.731564Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a6ff96b3-d199-4bb0-a5d8-d0ed4e819bfa · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Communication-efficient learning of deep networks from decentralized data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.225125Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 073c2c88-9ee8-4ea3-9752-c7d60bf91337 · outbound

This paper cites Federated learning for edge networks: Re- source optimization and incentive mechanism,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Federated learning for edge networks: Re- source optimization and incentive mechanism,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.210203Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation eff5d75f-1458-46d3-b2dd-6a578473f095 · outbound

This paper cites Swarm intelligence- based task scheduling for enhancing security for IoT devices,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Swarm intelligence- based task scheduling for enhancing security for IoT devices,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.194246Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 139e4b94-c57b-4571-b099-441f2d4f0976 · outbound

This paper cites A review of rechargeable batteries for portable electronic devices,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT A review of rechargeable batteries for portable electronic devices,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.179257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b36ad18d-fd39-406c-b65a-72e7e357960b · outbound

This paper cites Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.163772Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 035120f5-ab8e-4a3f-ad2c-bf44d80413d7 · outbound

This paper cites IEEE Transactions on Green Communications and Network- ing,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT IEEE Transactions on Green Communications and Network- ing,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.148829Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bfac93e6-b590-4e31-80e6-163e439c6289 · outbound

This paper cites To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.133565Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7f251d87-0ed4-4660-82fb-75e2dadfe676 · outbound

This paper cites Optimizing training efficiency and cost of hierarchical federated learning in heterogeneous mobile- edge cloud computing,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Optimizing training efficiency and cost of hierarchical federated learning in heterogeneous mobile- edge cloud computing,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-11T21:57:48.769604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f2550437-47f2-41bf-853d-80f957dfee11 · outbound

This paper cites Energy-efficient resource allocation for mobile-edge computation offloading,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Energy-efficient resource allocation for mobile-edge computation offloading,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.109972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9b943c16-609f-41e2-adac-5687fb7b9745 · outbound

This paper cites Federated learning over wireless networks: Optimization model design and analysis,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Federated learning over wireless networks: Optimization model design and analysis,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.095714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ab9d7fcb-004f-4bc7-b580-853e339ad230 · outbound

This paper cites Client-edge-cloud hierarchical federated learning,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Client-edge-cloud hierarchical federated learning,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.081444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4011dcb5-7ef6-4b4b-aed2-560bc146554f · outbound

This paper cites Client selection for federated learning with heterogeneous resources in mobile edge,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Client selection for federated learning with heterogeneous resources in mobile edge,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.066532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 5d498c0d-0608-4c6d-8c6c-5b79498a987b · outbound

This paper cites Wireless federated distillation for distributed edge learning with heterogeneous data,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Wireless federated distillation for distributed edge learning with heterogeneous data,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.052487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca13cde0-e22f-467d-b72b-de868129acee · outbound

This paper cites Sequential quadratic programming,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Sequential quadratic programming,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.038444Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8863b882-f1b1-4384-b5a5-07d9cd88cfdd · outbound

This paper cites Federated learning on non-iid data silos: An experimental study,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Federated learning on non-iid data silos: An experimental study,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.023833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation d5c271eb-a32f-4779-b029-c80e30c6a896 · outbound

This paper cites ”Ranking-based Client Imitation Selection for Efficient Federated Learning.” Forty-first International Conference on Machine Learning.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT ”Ranking-based Client Imitation Selection for Efficient Federated Learning.” Forty-first International Conference on Machine Learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:49.007918Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 8f9ff3fe-0f58-4c6d-bdb2-68ed20959102 · outbound

This paper cites Adaptive client selection in resource constrained federated learning systems: A deep reinforcement learning approach,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Adaptive client selection in resource constrained federated learning systems: A deep reinforcement learning approach,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:48.991848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b1afc9f2-5dd1-4f3b-afc8-d5765eea93ae · outbound

This paper cites Reinforcement and Imitation Learning via Interactive No-Regret Learning.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Reinforcement and Imitation Learning via Interactive No-Regret Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-11T21:57:48.813994Z

Source-reported events for the cited work

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Observation 71f7e210-e219-475a-84cd-5ae0217bf945 · outbound

This paper cites Optimal Completion Distillation for Sequence Learning.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Optimal Completion Distillation for Sequence Learning

Reference 20

Resolution: verified exact; local_arxiv, observed 2026-08-11T21:57:48.901271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation bac7e500-3d7c-4218-bff4-b1058e4d61a3 · outbound

This paper cites Model-contrastive federated learning,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Model-contrastive federated learning,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:48.976715Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1d0d29fa-a6c5-453a-91c3-b4c85fa0d70a · outbound

This paper cites Active Federated Learning.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Active Federated Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-11T21:57:48.829469Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10108253-bef5-414b-acba-5e31b3af1184 · outbound

This paper cites Tifl: A tier-based federated learning system,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Tifl: A tier-based federated learning system,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:48.961826Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f5f585f7-5613-42f4-b9da-8d364b01f5ca · outbound

This paper cites Optimizing federated learning on non-iid data with reinforcement learning,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT Optimizing federated learning on non-iid data with reinforcement learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:48.947055Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 11c89224-fcb0-41e9-933b-f3aaec554c2f · outbound

This paper cites FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning,.

BEFL: Balancing Energy Consumption in Federated Learning for Mobile Edge IoT FLASH-RL: Federated Learning Addressing System and Static Heterogeneity using Reinforcement Learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-11T21:57:48.932167Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.