Paper Citation Record · LEDGER

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models

As of 10 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2502.08008.

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

pith.paper-citation-record.v1
2502.08008 v2

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T11:13:58.916649Z

measured 28 of 28 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

28 of 28 outbound references displayed

verified exact0
verified fuzzy22
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation fc96d55b-9bb5-4870-91a9-9e925440b70f · outbound

This paper cites When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models When moe meets llms: Parameter efficient fine-tuning for multi-task medical applications,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.374174Z

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 571c7c6c-0ee2-40f6-a2b0-7e14cc5e1564 · outbound

This paper cites Reconstructing training data with informed adversaries,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Reconstructing training data with informed adversaries,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.363276Z

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 cf344155-46b3-41a3-bb5b-8767cb0917f6 · outbound

This paper cites Bounding training data reconstruction in dp-sgd,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Bounding training data reconstruction in dp-sgd,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.351896Z

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 5d46263b-6c12-4c60-a6a7-2f1ceac8706f · outbound

This paper cites Machine learning with membership privacy using adversarial regularization,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Machine learning with membership privacy using adversarial regularization,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.340379Z

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 91b02849-286f-4410-b4f4-c012eeb29369 · outbound

This paper cites Privacy risk in machine learning: Analyzing the connection to overfitting,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Privacy risk in machine learning: Analyzing the connection to overfitting,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.328750Z

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 9ce7542f-1005-4c1a-9a38-3ab219b27158 · outbound

This paper cites The secret sharer: Evaluating and testing unintended memorization in neural networks,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models The secret sharer: Evaluating and testing unintended memorization in neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.315434Z

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 f61e2055-ca06-40af-9836-ff24d11936d9 · outbound

This paper cites Ex- tracting training data from large language models,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Ex- tracting training data from large language models,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.302571Z

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 cc2111c0-e726-47fd-890f-e410dc43aa0c · outbound

This paper cites Calibrating noise to sensitivity in private data analysis,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Calibrating noise to sensitivity in private data analysis,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.291784Z

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 e36be104-4bba-4361-b25c-847f51b23ae1 · outbound

This paper cites Limits of computa- tional differential privacy in the client/server setting,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Limits of computa- tional differential privacy in the client/server setting,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.280226Z

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 2e9bd173-ae6d-400f-8120-acdc1f4c08c0 · outbound

This paper cites Applied Federated Learning: Improving Google Keyboard Query Suggestions.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Applied Federated Learning: Improving Google Keyboard Query Suggestions

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.847312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1ab4b796-4b46-4343-a790-fb5e8214eaed · outbound

This paper cites Differentially pri- vate stochastic gradient descent with fixed-size minibatches: Tighter RDP guarantees with or without replacement,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Differentially pri- vate stochastic gradient descent with fixed-size minibatches: Tighter RDP guarantees with or without replacement,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.268856Z

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 98f8c532-38a0-4114-83d9-073858bf5b9d · outbound

This paper cites R ´enyi differential privacy,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models R ´enyi differential privacy,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.256188Z

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 fed9f81f-0c8a-4f24-8c1d-55cc73a83663 · outbound

This paper cites Client selection in federated learning: Principles, challenges, and opportunities,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Client selection in federated learning: Principles, challenges, and opportunities,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.859471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ca20ec-f454-4875-a278-38e908746abd · outbound

This paper cites Vehicle selection and resource optimization for federated learning in vehicular edge computing,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Vehicle selection and resource optimization for federated learning in vehicular edge computing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.237168Z

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 7d59c149-b392-48ce-989f-0fee82319200 · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models GLUE: A multi-task benchmark and analysis platform for natural language understanding,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.224718Z

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 5da65edb-f8c9-46aa-90bd-efd554a48ca3 · outbound

This paper cites Ew- tune: A framework for privately fine-tuning large language models with differential privacy,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Ew- tune: A framework for privately fine-tuning large language models with differential privacy,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.211721Z

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 2fdc81cb-ecb6-4630-bd61-32d7aabb480b · outbound

This paper cites Differentially Private Fine-tuning of Language Models.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Differentially Private Fine-tuning of Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.873653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a923853b-8a62-4b9e-b3fa-a8e2f0e5bc29 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.877372Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9c07951-3d69-4e3f-9021-bcafa92864c9 · outbound

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

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Communication-efficient learning of deep networks from decentral- ized data,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.090325Z

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 224909c1-ac47-413e-946f-45659c601539 · outbound

This paper cites The algorithmic foundations of differential privacy,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models The algorithmic foundations of differential privacy,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.078861Z

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 d908732e-e271-4847-b49e-e09e1ae566bb · outbound

This paper cites Deep learning with differential privacy,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Deep learning with differential privacy,

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.889739Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d09bb585-e3bb-4d30-bc9a-ed71009b1a67 · outbound

This paper cites Concentrated Differential Privacy.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Concentrated Differential Privacy

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T11:13:58.894146Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c715fb67-517b-46a1-8d99-7f0f339ff4fd · outbound

This paper cites https://opacus.ai/docs/introduction, Last accessed on 2025-1-8.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models https://opacus.ai/docs/introduction, Last accessed on 2025-1-8

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.059519Z

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 a34920db-fc52-4f09-98ee-f9316a7c56bd · outbound

This paper cites Privacy amplification by sub- sampling: Tight analyses via couplings and divergences,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Privacy amplification by sub- sampling: Tight analyses via couplings and divergences,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.047021Z

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 29b889c5-5b08-4b9a-8a49-f0811931c840 · outbound

This paper cites Subsampled r´enyi differential privacy and analytical moments accountant,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Subsampled r´enyi differential privacy and analytical moments accountant,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.034949Z

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 c29427eb-94e2-4f44-bd56-8fd747c30734 · outbound

This paper cites Bayesian differential privacy for ma- chine learning,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Bayesian differential privacy for ma- chine learning,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.022268Z

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 2d00bf06-bc54-48df-b874-2f80b1fa9ffa · outbound

This paper cites Superglue: A multi-task benchmark and analysis platform for natural language understanding,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Superglue: A multi-task benchmark and analysis platform for natural language understanding,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:59.009550Z

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 2eb36726-c627-412d-a921-408a9a4a4e1e · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank,.

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models Recursive deep models for semantic compositionality over a sentiment treebank,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T11:13:58.995620Z

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

No inbound Pith citation observations are available.