Paper Citation Record · LEDGER

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata

As of 8 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2607.28338.

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

pith.paper-citation-record.v1
2607.28338 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-31T10:49:44.518622Z

measured 40 of 40 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

40 of 40 outbound references displayed

verified exact0
verified fuzzy0
unresolved39
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 37983617-0e83-4d9c-a2e0-f322842d7f72 · outbound

This paper cites Deep learning with differential privacy.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Deep learning with differential privacy

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.259082Z

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Unavailable: canonical work link unavailable.

Observation 015b92f0-7507-4361-b66a-28833721e07b · outbound

This paper cites k-means++: The advantages of careful seeding.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata k-means++: The advantages of careful seeding

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.321771Z

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Observation 129df1ef-0ae5-4cef-ae20-0b1cf16645a3 · outbound

This paper cites A systematic survey on clustering in federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata A systematic survey on clustering in federated learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.354584Z

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Observation 1711d2a3-277f-4b51-a077-6ead8ff91134 · outbound

This paper cites A survey on clustered federated learning: Taxonomy, analysis and applications.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata A survey on clustered federated learning: Taxonomy, analysis and applications

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.409558Z

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Observation 90bbe04f-5829-43c9-be2f-49fac6f043b2 · outbound

This paper cites Practical secure aggregation for privacy-preserving machine learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Practical secure aggregation for privacy-preserving machine learning

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.470341Z

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Observation 27da4ca3-13fe-4bc9-bfee-4711dd8c9d30 · outbound

This paper cites Federated learning with hierarchical cluster- ing of local updates to improve training on non-iid data.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Federated learning with hierarchical cluster- ing of local updates to improve training on non-iid data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.531797Z

Source-reported events for the cited work

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Observation 1a9966f2-0193-4349-a56a-4dab68c7c924 · outbound

This paper cites Homomorphic encryption for arithmetic of approximate numbers.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Homomorphic encryption for arithmetic of approximate numbers

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.592388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ceeadf5f-c917-400c-af5b-49ac6e3d959e · outbound

This paper cites Maximum likelihood from incomplete data via the em algorithm.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Maximum likelihood from incomplete data via the em algorithm

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.623399Z

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Observation 52c34ce4-1589-4cb5-8f84-3c2707887c6a · outbound

This paper cites Heterogeneity for the win: One-shot federated clustering.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Heterogeneity for the win: One-shot federated clustering

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.683462Z

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Unavailable: canonical work link unavailable.

Observation e71442b0-59f4-4c5a-9789-49038a8b58a8 · outbound

This paper cites Concept decompositions for large sparse text data using clustering.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Concept decompositions for large sparse text data using clustering

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.745727Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8090d542-5d66-44b2-9f3e-dc0ff7821bbc · outbound

This paper cites Federated-em with heterogeneity mitigation and variance reduction.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Federated-em with heterogeneity mitigation and variance reduction

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.808712Z

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Unavailable: canonical work link unavailable.

Observation c4d5dbf1-32f2-48b6-b59f-33318378d63e · outbound

This paper cites Flexible clustered federated learning for client-level data distribution shift.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Flexible clustered federated learning for client-level data distribution shift

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.857400Z

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Unavailable: canonical work link unavailable.

Observation 8a360489-de62-47b7-8a32-c4ccc6fd3498 · outbound

This paper cites Lane, Marc Langheinrich, and Mar- tin Gjoreski.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Lane, Marc Langheinrich, and Mar- tin Gjoreski

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.908919Z

Source-reported events for the cited work

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Observation 0eafd5a0-25f6-4603-a8bc-ad6cec7bec92 · outbound

This paper cites An efficient framework for clustered federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata An efficient framework for clustered federated learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:42.960078Z

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Observation 70fe5fcc-4d86-44bb-8617-b4b7b68555a5 · outbound

This paper cites Robust Federated Learning in a Heterogeneous Environment.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Robust Federated Learning in a Heterogeneous Environment

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.018688Z

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Observation 845d2e44-0a31-4c1f-9de6-cff2814c5cc7 · outbound

This paper cites Deep neural networks with random gaussian weights: A universal classification strategy? IEEE Transactions on Signal Processing, 64(13):3444–3457, 2016.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Deep neural networks with random gaussian weights: A universal classification strategy? IEEE Transactions on Signal Processing, 64(13):3444–3457, 2016

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.072388Z

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Unavailable: canonical work link unavailable.

Observation a647bc8c-fbdf-4c34-99d8-10db37956b30 · outbound

This paper cites Delving deep into rectifiers: Surpassing human-level performance on imagenet classification.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Delving deep into rectifiers: Surpassing human-level performance on imagenet classification

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.102547Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 637df27b-9907-4cff-b071-76531c322372 · outbound

This paper cites Advances and open problems in federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Advances and open problems in federated learning

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.219669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70700bcb-97dc-4e49-b838-fec56f0aa976 · outbound

This paper cites Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Federated hyperparameter tuning: Challenges, baselines, and connections to weight-sharing

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.264452Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf830bd1-2a97-4110-8db0-630cc3d2bfc1 · outbound

This paper cites Recent advances on federated learning: A systematic survey.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Recent advances on federated learning: A systematic survey

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.301774Z

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Observation 261ec8e9-26db-43fa-a849-574dba544667 · outbound

This paper cites T-friedman test: A new statistical test for multiple comparison with an adjustable conservativeness measure.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata T-friedman test: A new statistical test for multiple comparison with an adjustable conservativeness measure

Reference 21

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.328606Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d89d34e-1024-4d1b-8d77-d8524600117f · outbound

This paper cites Multi-center federated learning: clients clustering for better personalization.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Multi-center federated learning: clients clustering for better personalization

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.387975Z

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Observation 6cb4ffe1-6956-44cc-a350-1215bd963311 · outbound

This paper cites Privacy- preserving clustering federated learning for non-iid data.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Privacy- preserving clustering federated learning for non-iid data

Reference 23

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.448761Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8cbf52b-68e9-4d7b-beda-7cb72942622d · outbound

This paper cites Structured federated learning through clustered additive modeling.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Structured federated learning through clustered additive modeling

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.508799Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0020e3a-dbb3-432f-a83b-c084c31d3fab · outbound

This paper cites Differentially private clustered federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Differentially private clustered federated learning

Reference 25

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.563778Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 54b839be-412f-46e2-9e52-cec75fdf263b · outbound

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

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Communication-efficient learning of deep networks from decentralized data

Reference 26

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.626004Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50dec6d2-375d-422d-93ab-2bd49786316d · outbound

This paper cites A survey on security and privacy of federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata A survey on security and privacy of federated learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.692234Z

Source-reported events for the cited work

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Observation 100c5898-d17a-453f-ba54-d17153caa83b · outbound

This paper cites Machine learning: a probabilistic perspective.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Machine learning: a probabilistic perspective

Reference 28

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.743021Z

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Observation db0a9593-a4e8-4d60-89c6-83d2a913eb1d · outbound

This paper cites Fedshe: privacy pre- serving and efficient federated learning with adaptive segmented ckks homomorphic encryption.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Fedshe: privacy pre- serving and efficient federated learning with adaptive segmented ckks homomorphic encryption

Reference 29

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.801827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46af694a-974b-4d8e-9a53-057532cb5a8b · outbound

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

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Clustered federated learning: Model- agnostic distributed multitask optimization under privacy constraints

Reference 30

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.860186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20760c2a-6c5c-4d51-83af-ab88773c0ea7 · outbound

This paper cites Contrastive encoder pre-training-based clustered federated learning for heterogeneous data.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Contrastive encoder pre-training-based clustered federated learning for heterogeneous data

Reference 31

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.915383Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6c22cb62-fdb5-4be6-9fda-a14641e88d78 · outbound

This paper cites Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Efficient distribution similarity identification in clustered federated learning via principal angles between client data subspaces

Reference 32

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:43.976263Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8043c1ea-3ca9-4b47-b7e2-1bb6f4465723 · outbound

This paper cites Fvfl: A flexible and verifiable privacy-preserving federated learning scheme.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Fvfl: A flexible and verifiable privacy-preserving federated learning scheme

Reference 33

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.048009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a51a228f-3401-43c5-9862-3158640039e8 · outbound

This paper cites Priver- ifl: Privacy-preserving and aggregation-verifiable federated learning.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Priver- ifl: Privacy-preserving and aggregation-verifiable federated learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.069500Z

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Unavailable: canonical work link unavailable.

Observation 81d8ca1b-b8a4-47d4-8509-e7e4ca132aaf · outbound

This paper cites Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification

Reference 35

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.108813Z

Source-reported events for the cited work

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Observation 231ad297-7b38-4123-b814-e70a23eed8b5 · outbound

This paper cites Heterogeneous federated learning: State-of-the-art and research challenges.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Heterogeneous federated learning: State-of-the-art and research challenges

Reference 36

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.141631Z

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Unavailable: canonical work link unavailable.

Observation e61b49c8-10d4-416c-9d73-330ca89c41cb · outbound

This paper cites Stocfl: A stochastically clustered federated learning framework for non-iid data with dynamic client participation.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Stocfl: A stochastically clustered federated learning framework for non-iid data with dynamic client participation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.251712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e38685d8-c41e-436f-a6c1-a9cedb17f8a1 · outbound

This paper cites Efficient clustering on encrypted data.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Efficient clustering on encrypted data

Reference 38

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.350521Z

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Unavailable: canonical work link unavailable.

Observation 3b8aa655-2a40-43b8-b038-8d3c234880e6 · outbound

This paper cites Em algorithms for gaussian mixtures with split-and-merge operation.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Em algorithms for gaussian mixtures with split-and-merge operation

Reference 39

Resolution: unresolved; no resolver link, observed 2026-07-31T10:49:44.410527Z

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Unavailable: canonical work link unavailable.

Observation 8d025f90-2121-4e72-a80d-34b410fbd677 · outbound

This paper cites an unresolved cited work.

Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata Unresolved cited work

Reference 40

Resolution: malformed identifier; no resolver link, observed 2026-07-31T10:49:44.518622Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.