Paper Citation Record · LEDGER

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices

As of 16 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2506.20644.

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

pith.paper-citation-record.v1
2506.20644 v2

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:49:42.310820Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+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

32 of 32 outbound references displayed

verified exact4
verified fuzzy9
unresolved19
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7159f530-ad75-4f80-b43c-ff4b39d2c96f · outbound

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

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:39.354754Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe52e191-2198-43bd-a163-e00d86029f6a · outbound

This paper cites Federated machine learning: Concept and applications,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Federated machine learning: Concept and applications,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:39.387446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 44ab4ca3-ece9-4dd5-b7fe-fdafff3e8042 · outbound

This paper cites an unresolved cited work.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-06T22:49:42.901760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b778db94-8399-4275-9dcc-5c00eaba37f9 · outbound

This paper cites Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.886007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4ded4f4b-4d93-490a-b2b1-52f846de2f10 · outbound

This paper cites Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.868491Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 26b9936f-3eae-4c9d-a484-cdf55ebe3a82 · outbound

This paper cites Group knowledge transfer: Federated learning of large cnns at the edge,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Group knowledge transfer: Federated learning of large cnns at the edge,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:39.848337Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e277647d-c83f-4112-b53a-7b82a0cbebf6 · outbound

This paper cites Federated learning: Challenges, methods, and future directions,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Federated learning: Challenges, methods, and future directions,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:39.922120Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d53c137a-0486-4004-a094-147cb5f84490 · outbound

This paper cites Federated learning in mobile edge networks: A comprehensive survey,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Federated learning in mobile edge networks: A comprehensive survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.842110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation adcfd7d1-84aa-439c-b719-ea7bcb8fa005 · outbound

This paper cites Advances and open problems in federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Advances and open problems in federated learning,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:40.149759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa4506b4-1791-47d7-bddb-ef926da983e4 · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices A survey on federated learning systems: Vision, hype and reality for data privacy and protection,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.816193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 04e719f3-4c04-4893-809a-143ed2ee9297 · outbound

This paper cites Federated Learning with Non-IID Data.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Federated Learning with Non-IID Data

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:40.301638Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1396d31e-176d-4a51-821a-e106da21c6b7 · outbound

This paper cites Model-contrastive federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Model-contrastive federated learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:40.452738Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1269f8e7-9ae3-4ac2-a999-15b5b42a3545 · outbound

This paper cites Federated optimization in heterogeneous networks,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Federated optimization in heterogeneous networks,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:40.662540Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e957bd54-f668-4bf8-9c27-a21586566657 · outbound

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

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Tackling the objective inconsistency problem in heterogeneous federated optimiza- tion,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:40.761881Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5f15a9f6-e9df-4617-b0ea-01d24e8a127a · outbound

This paper cites Dense: Data-free one-shot federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Dense: Data-free one-shot federated learning,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:41.039237Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 355e6afd-25a7-4740-94f2-c612d238fec3 · outbound

This paper cites Data-free knowledge distillation for het- erogeneous federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Data-free knowledge distillation for het- erogeneous federated learning,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:41.182872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b2e38360-203e-4f0c-903d-defeca836278 · outbound

This paper cites Towards fair federated learning with zero-shot data augmentation,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Towards fair federated learning with zero-shot data augmentation,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.751072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation cc3d385d-a3a0-48bf-b2fd-7c1147b5a6b2 · outbound

This paper cites Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Fine-tuning global model via data-free knowledge distillation for non-iid federated learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.734909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation d3e8c45b-1fdc-4a95-82a4-544227ce3b88 · outbound

This paper cites Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.718433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 37e28ecf-0e49-4800-b868-591277d83950 · outbound

This paper cites Model Pruning Enables Efficient Federated Learning on Edge Devices.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Model Pruning Enables Efficient Federated Learning on Edge Devices

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:42.452511Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 4134b9fc-858c-4e14-9649-49dd7a84fb63 · outbound

This paper cites DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices DapperFL: Domain Adaptive Federated Learning with Model Fusion Pruning for Edge Devices

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:42.430668Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation e789a4e1-db26-4da5-bb70-8ad23b63eaa0 · outbound

This paper cites Model compression for communication efficient federated learning,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Model compression for communication efficient federated learning,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.702303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation c892aa11-b4ed-4718-bf53-ad6347dde4e6 · outbound

This paper cites Efficient Model Compression for Hierarchical Federated Learning.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Efficient Model Compression for Hierarchical Federated Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:41.988449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32bb05b5-fc03-43e4-871e-ce0e9636ff22 · outbound

This paper cites Bayesian Federated Model Compression for Communication and Computation Efficiency.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Bayesian Federated Model Compression for Communication and Computation Efficiency

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:42.392824Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 069cdf5c-cd64-4eb3-8c65-1b154437fcc4 · outbound

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

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Learning multiple layers of features from tiny images,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.218843Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79b22e14-a537-4c5e-983c-627480f45283 · outbound

This paper cites Fake It Till Make It: Federated Learning with Consensus-Oriented Generation.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Fake It Till Make It: Federated Learning with Consensus-Oriented Generation

Reference 27

Resolution: verified exact; local_arxiv, observed 2026-08-06T22:49:42.371210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 29bc3c89-67a2-4e03-acb5-9aa5e3805694 · outbound

This paper cites Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.288672Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e8d8aa9-7ce4-4893-a6d2-a409519b1284 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.292688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2225a9db-2a60-4867-9204-fd8b80391219 · outbound

This paper cites Deep residual learning for image recognition,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Deep residual learning for image recognition,

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.297084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bc83d937-9619-4ddb-9065-1b6e6c7491e4 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices U-net: Convolutional networks for biomedical image segmentation,

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.301606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04651fb4-0877-479d-9629-ec6ff5b976bc · outbound

This paper cites Rectified linear units improve restricted boltz- mann machines,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Rectified linear units improve restricted boltz- mann machines,

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-06T22:49:42.306396Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 893fde2c-9fa7-438f-946a-935ae5e43eea · outbound

This paper cites Learning to attack federated learning: A model-based reinforcement learning attack framework,.

Efficient Federated Learning with Encrypted Data Sharing for Data-Heterogeneous Edge Devices Learning to attack federated learning: A model-based reinforcement learning attack framework,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T22:49:42.645097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.