Paper Citation Record · LEDGER

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 0 inbound Pith citation observations for arXiv:2501.18659.

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

pith.paper-citation-record.v1
2501.18659 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T00:22:30.780057Z

measured 37 of 37 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+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

37 of 37 outbound references displayed

verified exact2
verified fuzzy16
unresolved19
parse uncertain0
malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f71c327a-56f5-47d3-9ab6-9b2e058fdca5 · outbound

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

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Communication-efficient learning of deep networks from decentralized data,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.169871Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 996c57d5-900d-4f8c-ae61-32966219f220 · outbound

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

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Advances and open problems in federated learning,

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.649878Z

Source-reported events for the cited work

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Observation 7098b309-485f-4bce-9fe3-13d0d36be2fa · outbound

This paper cites Robust Federated Learning in a Heterogeneous Environment.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Robust Federated Learning in a Heterogeneous Environment

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.653778Z

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Observation c2c747fb-d527-4636-bd61-b15fdb551261 · outbound

This paper cites Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Clustered federated learning: Model-agnostic distributed multitask optimization under privacy con- straints,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.152548Z

Source-reported events for the cited work

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Observation 110bb5c2-7735-499a-9175-350ec7f6f43e · outbound

This paper cites An efficient frame- work for clustered federated learning,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning An efficient frame- work for clustered federated learning,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.141908Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d86c7cab-3194-4b99-b71c-6c7fa704b9c3 · outbound

This paper cites Pfa: Privacy-preserving federated adaptation for effective model personalization,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Pfa: Privacy-preserving federated adaptation for effective model personalization,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.131163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f18f6cd8-2539-4dc6-b1e9-7579a2f32e59 · outbound

This paper cites Resource- efficient federated learning with hierarchical aggregation in edge com- puting,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Resource- efficient federated learning with hierarchical aggregation in edge com- puting,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.119218Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 717af53b-7927-42be-a9bc-b7c1a1d74c84 · outbound

This paper cites Learning effi- cient convolutional networks through network slimming,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Learning effi- cient convolutional networks through network slimming,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.108676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30a4d608-7f31-434c-b3b7-5340e7887749 · outbound

This paper cites FedBN: Federated Learning on Non-IID Features via Local Batch Normalization.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning FedBN: Federated Learning on Non-IID Features via Local Batch Normalization

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.678750Z

Source-reported events for the cited work

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Observation 5a466970-c1eb-4080-b92f-95b7a575a6d3 · outbound

This paper cites Think Locally, Act Globally: Federated Learning with Local and Global Representations.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Think Locally, Act Globally: Federated Learning with Local and Global Representations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.682868Z

Source-reported events for the cited work

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Observation db5072c7-6f48-4d04-9a8f-a7fb6e8bb860 · outbound

This paper cites Hermes: an efficient federated learning framework for heterogeneous mobile clients,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Hermes: an efficient federated learning framework for heterogeneous mobile clients,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.098164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e252f602-0f56-4223-9dbc-53aea5c50225 · outbound

This paper cites Channel pruning for accelerating very deep neural networks,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Channel pruning for accelerating very deep neural networks,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.086413Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation fdeaedc3-8cf7-41ab-a5c5-b39424a9730c · outbound

This paper cites The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.694415Z

Source-reported events for the cited work

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Observation 8eff10bb-fdb6-48cc-b423-8249583f55ec · outbound

This paper cites Rethinking the Value of Network Pruning.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Rethinking the Value of Network Pruning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.698126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8d083137-a23e-47d7-81b0-6e623c6fa933 · outbound

This paper cites Federated Optimization: Distributed Machine Learning for On-Device Intelligence.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated Optimization: Distributed Machine Learning for On-Device Intelligence

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.701877Z

Source-reported events for the cited work

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Observation 06d4ec82-d2ab-407a-8468-dbeaffb570b5 · outbound

This paper cites Federated Learning for Mobile Keyboard Prediction.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated Learning for Mobile Keyboard Prediction

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.705583Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 01a60cce-616e-4101-bd7a-cbd04610c81b · outbound

This paper cites Federated Learning with Non-IID Data.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated Learning with Non-IID Data

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.709270Z

Source-reported events for the cited work

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Observation a94522b6-d5ba-431f-a1b6-dd8218de67b5 · outbound

This paper cites Federated optimization in heterogeneous networks,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated optimization in heterogeneous networks,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.075947Z

Source-reported events for the cited work

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Observation 0498fd2b-925a-4ce0-9f29-e547f129d6be · outbound

This paper cites Federated multi-task learning,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated multi-task learning,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.716356Z

Source-reported events for the cited work

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Observation aad51399-627c-491b-bec7-9fc372f89530 · outbound

This paper cites Federated Learning with Personalization Layers.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated Learning with Personalization Layers

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.720013Z

Source-reported events for the cited work

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Observation c6ccb2b3-8097-4e3d-9181-99378fd40552 · outbound

This paper cites Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Personalized federated learning with theoretical guarantees: A model-agnostic meta-learning approach,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.723703Z

Source-reported events for the cited work

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Observation e2b9a081-ffae-458c-a3a9-e7ed20fbdc9f · outbound

This paper cites Distributed pruning towards tiny neural networks in federated learning,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Distributed pruning towards tiny neural networks in federated learning,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.052407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 30fddcfe-4155-482b-aa7c-1350568f6b28 · outbound

This paper cites FedPrune: Towards Inclusive Federated Learning.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning FedPrune: Towards Inclusive Federated Learning

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-08-10T00:22:30.861644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 36f5266d-2772-4981-b222-ae3be6471bc7 · outbound

This paper cites Fedrolex: Model- heterogeneous federated learning with rolling sub-model extraction,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Fedrolex: Model- heterogeneous federated learning with rolling sub-model extraction,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.734238Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 756310b4-8ae4-4c64-a8ef-4eed17addc1a · outbound

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

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Model pruning enables efficient federated learning on edge devices,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.034509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ec5f2709-8fec-4ca7-8534-ee07bf6dab34 · outbound

This paper cites Personalized federated learning by structured and unstructured pruning under data heterogeneity,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Personalized federated learning by structured and unstructured pruning under data heterogeneity,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:31.022317Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 27f53512-aaa6-47b8-84ec-0ecaea959d28 · outbound

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

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Fedpe: Adaptive model pruning-expanding for federated learning on mobile devices,

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.745361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72dfc2bf-ed3a-44d9-843d-47f6f5e776d3 · outbound

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

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Fedmp: Federated learning through adaptive model pruning in heterogeneous edge computing,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.749002Z

Source-reported events for the cited work

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Observation c62490a4-0bc1-4a1f-8ff3-00f7e79ed540 · outbound

This paper cites Accelerating federated learning for iot in big data analytics with pruning, quantization and selective updating,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Accelerating federated learning for iot in big data analytics with pruning, quantization and selective updating,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:30.997005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f6fdfd8d-907a-4cff-a0f5-d841efdd6dca · outbound

This paper cites FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning FedDUAP: Federated Learning with Dynamic Update and Adaptive Pruning Using Shared Data on the Server

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.756094Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07bc99ea-9027-47bb-828f-5ccbf66c101a · outbound

This paper cites Federated learning with hierarchical clustering of local updates to improve training on non-IID data,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Federated learning with hierarchical clustering of local updates to improve training on non-IID data,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:30.985684Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation acc5adb7-0d6b-43a2-9fa8-9ee2f443ab0f · outbound

This paper cites Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.763106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1047ea1d-0141-4484-886d-cd3aa16ced9c · outbound

This paper cites EIE: Efficient inference engine on compressed deep neural network,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning EIE: Efficient inference engine on compressed deep neural network,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:30.974759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8fef01b-a8e7-4cf5-95ca-1f63b9e9e5f8 · outbound

This paper cites SNIP: Single-shot Network Pruning based on Connection Sensitivity.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning SNIP: Single-shot Network Pruning based on Connection Sensitivity

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.769935Z

Source-reported events for the cited work

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Observation 10d0d0a6-bcc9-4958-a3df-d87acf75bce2 · outbound

This paper cites Learning structured sparsity in deep neural networks,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Learning structured sparsity in deep neural networks,

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T00:22:30.773427Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35bf79a6-08e6-439c-9808-0d759f03c53f · outbound

This paper cites Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning Gate decorator: Global filter pruning method for accelerating deep convolutional neural networks,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T00:22:30.957865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5c366b1e-59cc-4da1-b914-784e0304f01d · outbound

This paper cites SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning.

SAFL: Structure-Aware Personalized Federated Learning via Client-Specific Clustering and SCSI-Guided Model Pruning SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

Reference 37

Resolution: verified exact; local_arxiv, observed 2026-08-10T00:22:30.816687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Pith citing papers

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