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Paper Citation Record · LEDGER

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data

As of 11 August 2026, this Paper Citation Record lists 46 of 46 outbound references and 0 inbound Pith citation observations for arXiv:2506.18259.

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

pith.paper-citation-record.v1
2506.18259 v1

Coverage vector

measured 46 of 46 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:29:47.227217Z

measured 46 of 46 standing notices

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Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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

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Reference resolution

46 of 46 outbound references displayed

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  • verified fuzzy16
  • unresolved29
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Outbound references

Observation c3f6204e-dd8f-408a-8465-ef5d34f23ed4 · outbound

This paper cites Automatic Healthcare Diagnosis and Prediction Assessment based on AI Multi-Classification Algorithm,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Automatic Healthcare Diagnosis and Prediction Assessment based on AI Multi-Classification Algorithm,

Reference 1

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Observation 5f531f16-3bed-4723-8292-96d2d8622278 · outbound

This paper cites Federated Learning for Object Detection in Autonomous Ve- hicles,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Federated Learning for Object Detection in Autonomous Ve- hicles,

Reference 2

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Observation f4951c66-45bf-42fd-a567-245243a85492 · outbound

This paper cites Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Privacy-Preserving Traffic Flow Prediction: A Federated Learning Approach,

Reference 3

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Observation e6633183-979b-480f-ad19-040585a277dd · outbound

This paper cites FELIDS: Federated learning-based intrusion detection sys- tem for agricultural Internet of Things,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data FELIDS: Federated learning-based intrusion detection sys- tem for agricultural Internet of Things,

Reference 4

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Observation 1eaae6f4-cb4f-4c64-ae12-d340787a15c1 · outbound

This paper cites Communication-efficient Learning of Deep Networks from Decentral- ized Data,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Communication-efficient Learning of Deep Networks from Decentral- ized Data,

Reference 5

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Observation 6003660c-d1ae-4240-8138-4dfc8cc214c9 · outbound

This paper cites Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Decentralized Edge Intelligence: A Dynamic Resource Allocation Framework for Hierarchical Federated Learning,

Reference 6

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Observation 076c9222-49ad-4a98-b800-31fa015314bc · outbound

This paper cites Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Dynamic Edge Association and Resource Allocation in Self-Organizing Hierarchical Federated Learning Networks,

Reference 7

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Observation 7ed96ede-3d11-4c06-9327-b197da64e1b5 · outbound

This paper cites Reputation-Aware Hedonic Coalition Formation for Ef- ficient Serverless Hierarchical Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Reputation-Aware Hedonic Coalition Formation for Ef- ficient Serverless Hierarchical Federated Learning,

Reference 8

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Observation b998ee30-5c00-4e9b-88b8-fe3dfe6f4874 · outbound

This paper cites Adaptive Hierar- chical Federated Learning Over Wireless Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Adaptive Hierar- chical Federated Learning Over Wireless Networks,

Reference 9

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Observation d96baccb-926f-4daf-85f1-82bce6f9e609 · outbound

This paper cites Client-Edge-Cloud Hi- erarchical Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Client-Edge-Cloud Hi- erarchical Federated Learning,

Reference 10

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Observation 06342c11-8427-4907-954e-ab075632b85d · outbound

This paper cites Bilateral pricing for dynamic association in federated edge learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Bilateral pricing for dynamic association in federated edge learning,

Reference 11

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Observation 46e2e65c-19e9-4f97-915f-1a7ef1c8d23a · outbound

This paper cites Federated Learning With Non-IID Data: A Survey,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Federated Learning With Non-IID Data: A Survey,

Reference 12

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Observation 73005bb2-baa6-482c-b2be-f652595ec85d · outbound

This paper cites IOFL: Intelligent-Optimization-Based Federated Learning for Non-IID Data,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data IOFL: Intelligent-Optimization-Based Federated Learning for Non-IID Data,

Reference 13

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Observation 9d742f5d-37ab-4515-8b29-47dd0a5217c5 · outbound

This paper cites Delayed Gradient Aver- aging: Tolerate the Communication Latency for Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Delayed Gradient Aver- aging: Tolerate the Communication Latency for Federated Learning,

Reference 14

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Observation f0aebcd5-0bc1-4205-82bf-f4f2654d1306 · outbound

This paper cites Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Reference 15

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source=pdf_text observed=2026-08-06T23:29:42.210170Z digest=sha256:978dae66194d891fd6f751fa9289901db3510e66012a5b3ca9b1541c3bcbec9b

Observation 03712b18-dac2-425e-9d4d-f66f042a1d31 · outbound

This paper cites QSGD: Communication-Efficient SGD via Gradient Quantization and Encod- ing,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data QSGD: Communication-Efficient SGD via Gradient Quantization and Encod- ing,

Reference 16

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Observation 15ba6895-a4c4-4d05-9f88-ae7a56f188af · outbound

This paper cites A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data A Joint Learning and Communications Framework for Federated Learning Over Wireless Networks,

Reference 17

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Observation fcef046a-61f1-468d-8fc9-47839e2210ca · outbound

This paper cites Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Federated Learning Over Wireless IoT Networks With Optimized Communication and Resources,

Reference 18

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Observation 96a7dbe7-4705-4c89-87e3-8b891eb9b4d6 · outbound

This paper cites Resource Allocation for Multi-Task Federated Learning Algorithm over Wireless Communication Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Resource Allocation for Multi-Task Federated Learning Algorithm over Wireless Communication Networks,

Reference 19

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Observation 392263b0-11cb-45f0-9eec-8f106560ced7 · outbound

This paper cites Robust Federated Learning With Noisy Communication,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Robust Federated Learning With Noisy Communication,

Reference 20

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Observation 3a0f6a3d-9c7a-4fa4-9062-bf9603e2e5d3 · outbound

This paper cites Federated Learning in Mobile Edge Networks: A Comprehensive Survey,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Federated Learning in Mobile Edge Networks: A Comprehensive Survey,

Reference 21

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Observation d8e9df40-6431-40a4-89fd-8b62830910ad · outbound

This paper cites Robust Decentralized Federated Learning Using Collaborative Deci- sions,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Robust Decentralized Federated Learning Using Collaborative Deci- sions,

Reference 22

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Observation d86476c5-7e06-4487-964f-ffedb71f6109 · outbound

This paper cites Acceler- ating Federated Learning With Cluster Construction and Hierarchical Aggregation,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Acceler- ating Federated Learning With Cluster Construction and Hierarchical Aggregation,

Reference 23

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Observation e57263e0-91d1-46f2-8dfe-a21409a66777 · outbound

This paper cites Resource Efficient Cluster-Based Federated Learning for D2D Communications,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Resource Efficient Cluster-Based Federated Learning for D2D Communications,

Reference 24

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Observation b28d89c9-1c88-428d-a7e3-7543d0f61766 · outbound

This paper cites Communication-efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Communication-efficient Hierarchical Federated Learning for IoT Heterogeneous Systems with Imbalanced Data,

Reference 25

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Observation 8920e582-be65-4979-bbeb-3e371dd6b79d · outbound

This paper cites Sample-level Data Selection for Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Sample-level Data Selection for Federated Learning,

Reference 26

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Observation a6b1ef8e-2d39-4c36-a9a9-bb29fb52c4c6 · outbound

This paper cites ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data ISFL: Federated Learning for Non-i.i.d. Data with Local Importance Sampling

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

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Observation 52bb42a2-e11b-47c7-ab2a-5e62db4e3018 · outbound

This paper cites FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data FedDC: Federated Learning with Non-IID Data via Local Drift Decoupling and Correction,

Reference 28

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Observation 9d6a3326-3029-4117-877e-96879e1167a7 · outbound

This paper cites Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Personalized Federated Learning for Intelligent IoT Applications: A Cloud-Edge Based Framework,

Reference 29

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Observation f895886c-4b6f-400b-9370-b2a01cd7b808 · outbound

This paper cites Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Hybrid-FL for Wireless Networks: Cooperative Learning Mechanism Using Non-IID Data,

Reference 30

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Observation ef221544-e08f-45ea-8be1-889f061a4940 · outbound

This paper cites Exploiting Unintended Feature Leakage in Collaborative Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Exploiting Unintended Feature Leakage in Collaborative Learning,

Reference 31

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source=pdf_text observed=2026-08-06T23:29:45.035865Z digest=sha256:cfb9c6288632a2aa79806b5b0ce38220aead5a663055f9d16be5d54dba072dcb

Observation 0129b47e-5711-4392-a2e4-586f13dc3a09 · outbound

This paper cites Federated Learning with Non-IID Data.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Federated Learning with Non-IID Data

Reference 32

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Observation 541290b5-9627-487a-92cd-3f73548d57e2 · outbound

This paper cites Feature Matching Data Synthesis for Non-IID Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Feature Matching Data Synthesis for Non-IID Federated Learning,

Reference 33

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Observation 2a32d3d2-e094-41d3-a331-909dff441ff6 · outbound

This paper cites Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Hierarchical Federated Learning ACROSS Heterogeneous Cellular Networks,

Reference 34

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Observation fbf9611a-598c-4b39-8170-36b83e67aec1 · outbound

This paper cites The Non-IID Data Quagmire of Decentralized Machine Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data The Non-IID Data Quagmire of Decentralized Machine Learning,

Reference 35

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:45.653627Z digest=sha256:5af4fe88403e76cf279903688638f83936ae1efd5132b9d30b7ab09793fba458

Observation 1dd73b9c-b72f-42eb-be8a-6a1992fb88ec · outbound

This paper cites Social-Trust and Power- Efficient Relay Selection for Device-to-Device Underlaying Distributed Shared Network,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Social-Trust and Power- Efficient Relay Selection for Device-to-Device Underlaying Distributed Shared Network,

Reference 36

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:50.494776Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:45.764262Z digest=sha256:723e97bfc98454f4ec5c340eff87198d12956abd68dcafc385ae77cc7ecad9ae

Observation c4618b6f-c93f-47d3-8645-66cd2bb89045 · outbound

This paper cites Energy-Efficiency Optimization- Based User Selection and Power Allocation for Uplink NOMA-Enabled IoT Networks,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Energy-Efficiency Optimization- Based User Selection and Power Allocation for Uplink NOMA-Enabled IoT Networks,

Reference 37

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:50.236587Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:45.969965Z digest=sha256:1d96e6b10be1b069bd8b4e2a7f17951b114cc6e18064fb0eb90f0ae13925a9da

Observation 8e5abf09-6fcb-4730-b060-b3ccb6eef90e · outbound

This paper cites Node Selection Strategy Design Based on Reputation Mechanism for Hierarchical Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Node Selection Strategy Design Based on Reputation Mechanism for Hierarchical Federated Learning,

Reference 38

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:49.883758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.024274Z digest=sha256:e70eba060b0c4910c49b3d5a05790a8cef8d7ebae407bca0349cbefb8918460b

Observation c12cd375-9207-4e0b-a010-2a7a28491955 · outbound

This paper cites Pytorch Conditional CGAN.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Pytorch Conditional CGAN

Reference 39

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:49.680557Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.301939Z digest=sha256:32a21b4a109234deaad982a41e51ce7d2544a5832bed69fa9e0845baaa47229f

Observation 92e13630-e2ea-4608-a837-dc53c1a12eeb · outbound

This paper cites CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data CIFAKE: Image Classification and Explainable Identification of AI-Generated Synthetic Images,

Reference 40

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:49.403616Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.449826Z digest=sha256:6573e7760f05b9b84c566dcc22b6fbcbbd791536f397e1e1a9d6c06891d3f319

Observation f0f16610-75df-48ae-b473-21ec8d403fc0 · outbound

This paper cites Risk Neutral is Best for Risky Decision Making,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Risk Neutral is Best for Risky Decision Making,

Reference 41

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:49.139778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.615764Z digest=sha256:870f88d183c59c54267f77e7c587ad322b55f4094198cc90633f00927e99af72

Observation 1226a8c2-104b-488b-9e7a-a80e83803901 · outbound

This paper cites Engwerda, LQ Dynamic Optimization and Differential Games.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Engwerda, LQ Dynamic Optimization and Differential Games

Reference 42

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:48.872134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.686892Z digest=sha256:50bafdec09627da43fd68f81d9065a205c0869638532ad2e9bda3d5c7ef32a38

Observation 3e53343d-8295-4c36-b268-1b056711db65 · outbound

This paper cites Lyapunov Stability Theory,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Lyapunov Stability Theory,

Reference 43

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:48.627393Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.785032Z digest=sha256:709bceb885bedae02a9c20e311658c5cfc8828d63117e309478e0da7c27d6933

Observation fa886bed-0ca9-4030-8c1d-39c1d5ee19b3 · outbound

This paper cites an unresolved cited work.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Unresolved cited work

Reference 44

Resolution
unresolved
raw_fallback, observed 2026-08-06T23:29:48.281130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:46.995273Z digest=sha256:cb714e7de3329b83d5337a3e833bb9f224687bd93c68e0d8003c5c0510e17435

Observation d4d6f168-0147-4cfd-961c-87c495edfd53 · outbound

This paper cites Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edge,

Reference 45

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:48.032562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:47.109249Z digest=sha256:ee532d8bfb167ffc8d5673105567896620f3d337b2529a44919359ff2a0b3560

Observation 621616cc-d119-4747-a2ee-59210e5d5a6d · outbound

This paper cites FedMCCS: Mul- ticriteria Client Selection Model for Optimal IoT Federated Learning,.

Edge Association Strategies for Synthetic Data Empowered Hierarchical Federated Learning with Non-IID Data FedMCCS: Mul- ticriteria Client Selection Model for Optimal IoT Federated Learning,

Reference 46

Resolution
verified fuzzy
raw_fallback, observed 2026-08-06T23:29:47.756610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-08-06T23:29:47.227217Z digest=sha256:963b7e15a781538ed91748de79ec4e8dfb61bc2462837ac5041c79dc9b1c0429

Pith citing papers

No inbound Pith citation observations are available.