Paper Citation Record · LEDGER

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design

As of 8 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2508.01745.

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

pith.paper-citation-record.v1
2508.01745 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T05:32:32.720263Z

measured 28 of 28 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

28 of 28 outbound references displayed

verified exact0
verified fuzzy23
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 6fafd782-abfe-4e5c-9cf5-80fec41a0387 · outbound

This paper cites Sampling, communica- tion, and prediction co-design for synchronizing the real-world device and digital model in metaverse,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Sampling, communica- tion, and prediction co-design for synchronizing the real-world device and digital model in metaverse,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.180857Z

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.

Observation 4dc83133-c09b-40f2-8123-ea95393db8af · outbound

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

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Communication-efficient learning of deep networks from decentralized data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.165425Z

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.

Observation 8338225e-72a9-4b3f-bbe8-7958c58f27a1 · outbound

This paper cites Model pruning enables efficient federated learning on edge devices,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Model pruning enables efficient federated learning on edge devices,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.150960Z

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.

Observation 0773e3fd-eab7-4570-bb69-3e01e8cf3f09 · outbound

This paper cites Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Efficient federated learning for metaverse via dynamic user selection, gradient quantization and resource allocation,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.134433Z

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.

Observation 319f9e02-6598-47f1-a31a-b61022520e45 · outbound

This paper cites Joint gradient sparsifica- tion and device scheduling for federated learning,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Joint gradient sparsifica- tion and device scheduling for federated learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.119628Z

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.

Observation d993d4f5-9231-4a9d-95a1-a61709dbb100 · outbound

This paper cites Generative ai for integrated sensing and communication: Insights from the physical layer perspective,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Generative ai for integrated sensing and communication: Insights from the physical layer perspective,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.105490Z

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.

Observation 9a632429-eb47-4d04-ac9f-9008545e34bf · outbound

This paper cites Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Generative AI Enabled Robust Data Augmentation for Wireless Sensing in ISAC Networks

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:32.621505Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d3a9f042-4dff-492c-a9d0-0d9d8796860c · outbound

This paper cites Generative adversarial networks,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Generative adversarial networks,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.088293Z

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.

Observation e2934cef-4617-4332-ba5a-6b755690a615 · outbound

This paper cites A survey on variational autoencoders in recommender systems,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design A survey on variational autoencoders in recommender systems,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:32.631110Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e649eef-d49e-4786-bb65-350c8c184a4b · outbound

This paper cites Fedvae: Trajectory privacy preserving based on federated variational autoencoder,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Fedvae: Trajectory privacy preserving based on federated variational autoencoder,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.074176Z

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.

Observation f8bba228-9ef5-4cd3-aa57-edc1cd03abe4 · outbound

This paper cites A distributed generative adversarial network for data augmentation under vertical federated learning,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design A distributed generative adversarial network for data augmentation under vertical federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.059384Z

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.

Observation 97a8586c-e74f-4a8d-b850-63d3c63962b3 · outbound

This paper cites Filling the missing: Exploring generative ai for enhanced federated learning over heterogeneous mobile edge devices,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Filling the missing: Exploring generative ai for enhanced federated learning over heterogeneous mobile edge devices,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.042823Z

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.

Observation 905b2984-534e-4aea-98d8-0b46082002e7 · outbound

This paper cites Enhancing federated learning performance on heterogeneous iot devices using generative artificial intelligence with resource scheduling,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Enhancing federated learning performance on heterogeneous iot devices using generative artificial intelligence with resource scheduling,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.027565Z

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.

Observation 561ddb81-83eb-47d5-94e4-d4629304c42d · outbound

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

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design To talk or to work: Flexible communication compression for energy efficient federated learning over heterogeneous mobile edge devices,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:33.012624Z

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.

Observation 5e019455-de78-4eac-9e9d-86aa65aa46d2 · outbound

This paper cites En- ergy and spectrum efficient federated learning via high-precision over- the-air computation,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design En- ergy and spectrum efficient federated learning via high-precision over- the-air computation,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.996290Z

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.

Observation 29799891-8b2e-4f68-88d2-77e059ba983b · outbound

This paper cites Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Energy efficient federated learning over heterogeneous mobile devices via joint design of weight quantization and wireless transmission,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.979946Z

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.

Observation a8c34cda-9658-4d2d-b641-b2f3eb892c33 · outbound

This paper cites Efficient federated learning in resource-constrained edge intelligence networks using model compression,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Efficient federated learning in resource-constrained edge intelligence networks using model compression,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.962638Z

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.

Observation f35bb84d-0b13-407f-b8d6-d129a4a135f7 · outbound

This paper cites On the convergence of fedavg on non-iid data,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design On the convergence of fedavg on non-iid data,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.946567Z

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.

Observation 064b94a0-fb5f-4fba-acab-b0fe7e1994db · outbound

This paper cites Joint model pruning and device selection for communication-efficient federated edge learning,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Joint model pruning and device selection for communication-efficient federated edge learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.929161Z

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.

Observation 975fc94c-f487-4d3b-8b04-e62312cc42de · outbound

This paper cites A joint learning and communications framework for federated learning over wireless networks,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design A joint learning and communications framework for federated learning over wireless networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.910867Z

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.

Observation 2cd97f28-bc00-4b16-b63a-0dcebf86694c · outbound

This paper cites Sparsified sgd with mem- ory,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Sparsified sgd with mem- ory,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.895593Z

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.

Observation 1754c185-caaf-4d4c-8e04-b4828bbb458b · outbound

This paper cites Design and analysis of uplink and downlink communications for federated learning,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Design and analysis of uplink and downlink communications for federated learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.879211Z

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.

Observation 4af52d69-ffa5-4a29-a036-fb26d95019d3 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.861411Z

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.

Observation 42a04151-b59e-4969-aec7-7a2d6c3de966 · outbound

This paper cites Quantized federated learning under transmission delay and outage constraints,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Quantized federated learning under transmission delay and outage constraints,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.846274Z

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.

Observation bb046978-5193-4b86-a539-bc3efcc87d46 · outbound

This paper cites Learning multiple layers of features from tiny images,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Learning multiple layers of features from tiny images,

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:32.706009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 261dca5f-3177-4b9a-8d8f-06359acb3f1c · outbound

This paper cites Deep residual learning for image recognition,.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Deep residual learning for image recognition,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:32.710830Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e24a986-c851-4ae0-ad20-b39d572d6dfa · outbound

This paper cites Denoising Diffusion Step-aware Models.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design Denoising Diffusion Step-aware Models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T05:32:32.715644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation edec5716-c73e-49e7-a1d8-f66a33176f85 · outbound

This paper cites degree in Electronic Engineering with the Department of Electronic Engineering, Tsinghua University, Bei- jing, China.

Energy-Efficient Federated Learning for Edge Real-Time Vision via Joint Data, Computation, and Communication Design degree in Electronic Engineering with the Department of Electronic Engineering, Tsinghua University, Bei- jing, China

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T05:32:32.811491Z

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.

Pith citing papers

No inbound Pith citation observations are available.