Paper Citation Record · LEDGER

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

As of 18 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2604.20596.

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

pith.paper-citation-record.v1
2604.20596 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-10T00:29:43.521902Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-10T00:29:43.521902Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-10T00:29:46.932769Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy37
unresolved1
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation dc28dd6d-dbf0-4af9-a089-fb85799118f0 · outbound

This paper cites Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-05-10T00:29:46.934433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 48d00eca-e915-49d8-8b5e-874afb360dfe · outbound

This paper cites Federated Learning (FL) Overview of FL: At the start of each communication roundt, a global modelW t is provided by the server and a randomly sampled user setK t is constructed.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Federated Learning (FL) Overview of FL: At the start of each communication roundt, a global modelW t is provided by the server and a randomly sampled user setK t is constructed

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.504177Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4572a58a-8d89-448a-9ece-8ae95ebdb237 · outbound

This paper cites Overview Our proposed method PINA consists of two stages: (1) Cluster Model Initialization and (2) Clustered Model Training.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Overview Our proposed method PINA consists of two stages: (1) Cluster Model Initialization and (2) Clustered Model Training

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.490131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c95a18ee-6f51-4820-bc32-d8e9a9999e76 · outbound

This paper cites Fol- lowing [2, 31, 26], we simulate a cohort size of 10k with a smaller cohort size to achieve a more realistic signal-to-noise ratio which represents industry scale more closely.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Fol- lowing [2, 31, 26], we simulate a cohort size of 10k with a smaller cohort size to achieve a more realistic signal-to-noise ratio which represents industry scale more closely

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.496358Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation b73cf14f-5627-48f2-bee9-04e6030b3f8d · outbound

This paper cites an unresolved cited work.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-05-23T11:15:36.484517Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 58611dd8-c01e-4b7b-8425-c79cf4ef8368 · outbound

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

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Communication-efficient learning of deep net- works from decentralized data

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.500363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 05a05117-a72a-40a4-a395-b07444ab9122 · outbound

This paper cites Learning differentially private recurrent language models.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Learning differentially private recurrent language models

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.508891Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dd00ad72-caef-47a7-b63d-b743494188ef · outbound

This paper cites Calibrating noise to sensitivity in private data analy- sis.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Calibrating noise to sensitivity in private data analy- sis

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.480932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fa4031e5-81ed-4227-a41b-68e39eee8bfa · outbound

This paper cites What can we learn privately?.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation What can we learn privately?

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.549699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79903f06-76ee-430e-936b-40939cd94ad9 · outbound

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

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Practical secure aggregation for privacy- preserving machine learning

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.477277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d4f675d0-09b3-4288-a9ed-745aa43f8d77 · outbound

This paper cites Benchmarking secure sam- pling protocols for differential privacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Benchmarking secure sam- pling protocols for differential privacy

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.473150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fba7236e-cadd-416e-9a0f-62ce54a6174c · outbound

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

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Federated learning: Challenges, methods, and future directions

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.518705Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 66aa5807-f70b-4346-a43c-12a4e889d9d7 · outbound

This paper cites Federated learning with differential privacy: Algo- rithms and performance analysis.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Federated learning with differential privacy: Algo- rithms and performance analysis

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.530278Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 789510a0-e0af-47bd-b812-c633f2a27dee · outbound

This paper cites Differentially private federated learning on heterogeneous data.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Differentially private federated learning on heterogeneous data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.538594Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation bfff5f52-c686-44b9-8007-d8897a56f922 · outbound

This paper cites {PrivateFL}: Accurate, differentially private federated learning via personalized data transformation.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation {PrivateFL}: Accurate, differentially private federated learning via personalized data transformation

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.522575Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a1e15d3-2bf6-4bd3-89c2-fe299cd5f7ac · outbound

This paper cites An efficient framework for clustered federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation An efficient framework for clustered federated learning

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.561744Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d22e7f1-dbe2-47a6-a4c8-fd2e0ff8540d · outbound

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

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Clustered federated learning: Model-agnostic distributed multitask optimization under privacy constraints

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.514972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d0ac2a9a-fc77-4cd8-bfc8-1aa6c509e273 · outbound

This paper cites The algorithmic foun- dations of differential privacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation The algorithmic foun- dations of differential privacy

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.526209Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f90cd110-596f-4b0e-a28d-2b881112ba7f · outbound

This paper cites Deep learn- ing with differential privacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Deep learn- ing with differential privacy

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.534469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation de8456b4-23d2-4004-9e24-3602232b844d · outbound

This paper cites R ´enyi differential privacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation R ´enyi differential privacy

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.542347Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8e2d26a9-4724-4a93-8a36-a3f5888005ad · outbound

This paper cites Hypothesis testing interpretations and Renyi differential privacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Hypothesis testing interpretations and Renyi differential privacy

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.545972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation beead5b1-30e7-43b8-a21e-41133b3e7eb7 · outbound

This paper cites Privacy am- plification by subsampling: Tight analyses via couplings and divergences.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Privacy am- plification by subsampling: Tight analyses via couplings and divergences

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.603221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation dbf4c71c-c302-4aac-a847-944de6280825 · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation LoRA: Low-rank adaptation of large language models

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.619672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0195b001-240a-4741-9ca0-98781e22dba6 · outbound

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

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Efficient distribution similar- ity identification in clustered federated learning via principal angles between client data subspaces

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.594982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 79c9f96e-24c8-4f7a-afa9-02f5acf5763e · outbound

This paper cites Clustered feder- ated learning with adaptive local differential privacy on hetero- geneous IoT data.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Clustered feder- ated learning with adaptive local differential privacy on hetero- geneous IoT data

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.591336Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 36574306-289a-4772-bbcf-4755b9170605 · outbound

This paper cites Mitigating disparate impact of differential privacy in feder- ated learning through robust clustering.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Mitigating disparate impact of differential privacy in feder- ated learning through robust clustering

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.598717Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c47a143b-5707-4e69-a450-219c0c091bc2 · outbound

This paper cites Scaling language model size in cross-device federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Scaling language model size in cross-device federated learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.624362Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 11b3f9d2-bc31-45f2-9c34-95a059f064cf · outbound

This paper cites Secure aggregation for clus- tered federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Secure aggregation for clus- tered federated learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.632675Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 255e88f5-01dc-418a-8f4e-59e698df32e1 · outbound

This paper cites Clus- terguard: Secure clustered aggregation for federated learning with robustness.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Clus- terguard: Secure clustered aggregation for federated learning with robustness

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.577479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 03108170-ac7f-4f55-9149-df9e502dca3a · outbound

This paper cites Federated learning from pre-trained models: A contrastive learning approach.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Federated learning from pre-trained models: A contrastive learning approach

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.583574Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 74063e11-2fcd-4a22-8120-97bc65a464a8 · outbound

This paper cites DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation DP-DyLoRA: Fine-Tuning Transformer-Based Models On-Device under Differentially Private Federated Learning using Dynamic Low-Rank Adaptation

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-10T00:29:46.931363Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e773f537-17b7-4dc0-a9b9-6058a0af7cb5 · outbound

This paper cites Rethinking architecture design for tackling data heterogeneity in federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Rethinking architecture design for tackling data heterogeneity in federated learning

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.573722Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e05b5662-a0ad-4cb1-889e-ec284ca35d6c · outbound

This paper cites A hybrid approach to privacy-preserving federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation A hybrid approach to privacy-preserving federated learning

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.565188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation f67304ee-c022-41f3-869e-3d4b17056396 · outbound

This paper cites A comprehensive com- parison of multiparty secure additions with differential pri- vacy.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation A comprehensive com- parison of multiparty secure additions with differential pri- vacy

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.569222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a0b5131c-1d46-4d5d-9acf-955a47bc712e · outbound

This paper cites An analysis of variance test for normality (complete samples).

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation An analysis of variance test for normality (complete samples)

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.587655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a5d4c2bc-0ccd-4e17-affb-13508c01b2ea · outbound

This paper cites FLAIR: Federated learning annotated image repository.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation FLAIR: Federated learning annotated image repository

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.628456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 452082ce-81bd-42d8-8238-030f00837a95 · outbound

This paper cites Federated optimiza- tion in heterogeneous networks.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Federated optimiza- tion in heterogeneous networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.640468Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0b1ccbb2-81b0-4c37-81e9-0a839a238340 · outbound

This paper cites Tackling the objective inconsistency problem in heterogeneous federated optimization.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Tackling the objective inconsistency problem in heterogeneous federated optimization

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.636555Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation c388f186-b241-4c15-9eef-e7e3ca27d0e7 · outbound

This paper cites Scaffold: Stochastic controlled aver- aging for federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Scaffold: Stochastic controlled aver- aging for federated learning

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.553532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 01a3ea5e-2426-40df-8d6c-c03b80536d6c · outbound

This paper cites Breaking the centralized barrier for cross-device federated learning.

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation Breaking the centralized barrier for cross-device federated learning

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T11:15:36.557293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Pith citing papers

Observation dc28dd6d-dbf0-4af9-a089-fb85799118f0 · inbound