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Paper Citation Record · LEDGER

ModShift: Model Privacy via Designed Shifts

As of 16 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 0 inbound Pith citation observations for arXiv:2507.20060.

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

pith.paper-citation-record.v1
2507.20060 v1

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:01:29.009616Z

measured 22 of 22 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+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

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy10
  • unresolved12
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7cc1255c-46d8-4d6d-a3aa-4d0eccd92251 · outbound

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

ModShift: Model Privacy via Designed Shifts Advances and open problems in federated learning,

Reference 1

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no resolver link, observed 2026-08-15T18:01:28.877106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.877106Z digest=sha256:4feb64447d44ec4ef8047db1c398f41031b1361817901ba436f174026b3e28b9

Observation f2dfac2c-8788-43fc-ac8d-df704267e9df · outbound

This paper cites Privacy- preserving aggregation in federated learning: A survey,.

ModShift: Model Privacy via Designed Shifts Privacy- preserving aggregation in federated learning: A survey,

Reference 2

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.431906Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.883994Z digest=sha256:56fbcf04834bbc0e7f915ae61b6ba95b6d2964abd07a3a63f70eaafb221ad452

Observation fd593898-1118-4759-9b06-71bde0bc47ce · outbound

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

ModShift: Model Privacy via Designed Shifts Practical secure aggregation for privacy-preserving machine learning,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.410052Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.889327Z digest=sha256:e27a2734c4bc8e2e3053b9d77ba007892cf24a7372833e4695f7aa0b862edd14

Observation 277c0dbd-5889-4733-be66-d3fc4759b46a · outbound

This paper cites FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning.

ModShift: Model Privacy via Designed Shifts FastSecAgg: Scalable Secure Aggregation for Privacy-Preserving Federated Learning

Reference 4

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no resolver link, observed 2026-08-15T18:01:28.894461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.894461Z digest=sha256:eca4a0c6ee4e7ff7831cb2a00cea9df7f0392f15bb14aa41f047f7ee0e038e1e

Observation 9b8bda76-f637-4be0-b5f8-f672d48bd48d · outbound

This paper cites Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,.

ModShift: Model Privacy via Designed Shifts Lightsecagg: a lightweight and versatile design for secure aggregation in federated learning,

Reference 5

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no resolver link, observed 2026-08-15T18:01:28.900078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.900078Z digest=sha256:d70f11332091c4af3138c656a38ac8badd9d5cd246b11b43020b484e56b55a53

Observation e90f04e5-262e-4893-b0fb-3421297b07a5 · outbound

This paper cites Deep learning with differential privacy,.

ModShift: Model Privacy via Designed Shifts Deep learning with differential privacy,

Reference 6

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no resolver link, observed 2026-08-15T18:01:28.905232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.905232Z digest=sha256:3bf4c40ccbf108424434bc92dd89e9f72fec51253dc3e23fec5752284895c6e1

Observation 34ea1eb2-cf21-4555-bb26-9cdb3363de62 · outbound

This paper cites Approx- imating functions with approximate privacy for applications in signal estimation and learning,.

ModShift: Model Privacy via Designed Shifts Approx- imating functions with approximate privacy for applications in signal estimation and learning,

Reference 7

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.360462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.910298Z digest=sha256:defec0a3052f0644b6948b58d2c77313f6087c8db495c77fd9098edd52941bf2

Observation 21b55433-8e03-4156-b07a-f6ee2d9b52d1 · outbound

This paper cites Privfl: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks,.

ModShift: Model Privacy via Designed Shifts Privfl: Practical privacy-preserving federated regressions on high-dimensional data over mobile networks,

Reference 8

Resolution
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raw_fallback, observed 2026-08-15T18:01:29.339939Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.914865Z digest=sha256:4fd43dbdcf49c5ac105c04c53a475773bd8a64f5bade88bc15811a2498afd749

Observation 8273cdc0-1fbe-4bc9-996c-7497c2ce1eef · outbound

This paper cites An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning.

ModShift: Model Privacy via Designed Shifts An Accuracy-Lossless Perturbation Method for Defending Privacy Attacks in Federated Learning

Reference 9

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no resolver link, observed 2026-08-15T18:01:28.920440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.920440Z digest=sha256:65352359ba0ef34f3abe5e5e6226d5207d1239d7043ee0506657924dcefccc9c

Observation fbaf3483-5a5d-4828-8671-a0c9bb67fc29 · outbound

This paper cites On Model Protection in Federated Learning against Eavesdropping Attacks.

ModShift: Model Privacy via Designed Shifts On Model Protection in Federated Learning against Eavesdropping Attacks

Reference 10

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no resolver link, observed 2026-08-15T18:01:28.926113Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.926113Z digest=sha256:cef89182f869b761768d8707780ad8b0ae2dae9b6715fec94b60e0c4aa76cc57

Observation 650f8282-7b64-4007-bd42-eb69090a53b6 · outbound

This paper cites Guaranteed private communication with secret block structure,.

ModShift: Model Privacy via Designed Shifts Guaranteed private communication with secret block structure,

Reference 11

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raw_fallback, observed 2026-08-15T18:01:29.323663Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.931601Z digest=sha256:919cc8f170aeeb86abecd378b4877842d5dfc04325e4c80b18a5b402389d1011

Observation f6360e74-13d7-40c9-ae46-64c4cb295260 · outbound

This paper cites Channel state information-free location-privacy enhancement: Fake path injection,.

ModShift: Model Privacy via Designed Shifts Channel state information-free location-privacy enhancement: Fake path injection,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.306159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.937506Z digest=sha256:b9ec3664f3170f1adb0c8ad62742d3cacece4e107b4a1129a06ec14b255037cc

Observation 86d2d4e9-d60a-4d7b-b275-13c775d17d03 · outbound

This paper cites Channel state information-free location-privacy enhancement: Delay-angle information spoofing,.

ModShift: Model Privacy via Designed Shifts Channel state information-free location-privacy enhancement: Delay-angle information spoofing,

Reference 13

Resolution
verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.280483Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.944277Z digest=sha256:53d2af6cfcaeb1158bc5e39daa447e59740918f36c9e7facc9a7a87237342c72

Observation 8e5de3e6-421c-4631-a2b5-65e1cdee89d7 · outbound

This paper cites Optimized parameter design for channel state information-free location spoofing,.

ModShift: Model Privacy via Designed Shifts Optimized parameter design for channel state information-free location spoofing,

Reference 14

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.259655Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.951333Z digest=sha256:24be63b022321280f3cc139f6e6c0d00f474196c14cded68dd1b0302f96ad6b5

Observation b98df06c-8a0f-4195-9977-2abfbe126141 · outbound

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

ModShift: Model Privacy via Designed Shifts Communication-efficient learning of deep networks from decentralized data,

Reference 15

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.957615Z digest=sha256:9c23c2f2f83e052f19a5928e322b3d9d25e87e687f758393d5038e566970988b

Observation 9906c162-814c-4f86-b7b1-3a0bd1fe1e21 · outbound

This paper cites Maximal dissent: a state-dependent way to agree in distributed convex optimization,.

ModShift: Model Privacy via Designed Shifts Maximal dissent: a state-dependent way to agree in distributed convex optimization,

Reference 16

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.223038Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:28.963169Z digest=sha256:c550505d7b75e4f3a98f6b0a1964a8899d55c79de1d9758a72499f5f95a4a749

Observation 317c1689-f7b1-4628-8245-51c138830009 · outbound

This paper cites Scaffold: Stochastic controlled averaging for federated learn- ing,.

ModShift: Model Privacy via Designed Shifts Scaffold: Stochastic controlled averaging for federated learn- ing,

Reference 17

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no resolver link, observed 2026-08-15T18:01:28.968825Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.968825Z digest=sha256:b2a0b9d66db53ee1ae3eeb13af666923c33254f1a829c789cdbcd1641fffb5d6

Observation c29b6c8e-df16-47a7-b848-2f75a9387797 · outbound

This paper cites Feddc: Federated learning with non-iid data via local drift decoupling and correction,.

ModShift: Model Privacy via Designed Shifts Feddc: Federated learning with non-iid data via local drift decoupling and correction,

Reference 18

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.974647Z digest=sha256:a07c274e3738520c6e85b6cb2cd87e42b8d35341c70c3076fe9efeef7abb0dc8

Observation 2c399fc6-5c9e-4d07-b99b-dc37980b4730 · outbound

This paper cites Goldsmith, Wireless communications.

ModShift: Model Privacy via Designed Shifts Goldsmith, Wireless communications

Reference 19

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.985010Z digest=sha256:a78aa17680b16be0b84966e09bef97285a12baebb5be70754b99af9d773fada6

Observation 00961bb4-85fd-4498-8e2e-43e813837e3e · outbound

This paper cites an unresolved cited work.

ModShift: Model Privacy via Designed Shifts Unresolved cited work

Reference 20

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:28.994890Z digest=sha256:d16280865b1538abba1e7703b3d73ffe23e54a59eeba4a92a760baa02d472a55

Observation c537b0a8-9c0c-4a6e-9772-109918e6d686 · outbound

This paper cites an unresolved cited work.

ModShift: Model Privacy via Designed Shifts Unresolved cited work

Reference 21

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no resolver link, observed 2026-08-15T18:01:29.004212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:01:29.004212Z digest=sha256:3a068838c0b58816be17917ec51b829ac599ca1c3b5f361019dd96103ac7e4e6

Observation 03346c24-a63b-4adf-8b72-31aa8ebfb2c1 · outbound

This paper cites Pinchover and J.

ModShift: Model Privacy via Designed Shifts Pinchover and J

Reference 22

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verified fuzzy
raw_fallback, observed 2026-08-15T18:01:29.126672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-08-15T18:01:29.009616Z digest=sha256:6b917d5885592088519a1455f40c01d283f30dd5349453047df5f06ae5797002

Pith citing papers

No inbound Pith citation observations are available.