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Paper Citation Record · LEDGER

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks

As of 14 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 0 inbound Pith citation observations for arXiv:2601.04930.

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

pith.paper-citation-record.v1
2601.04930 v2

Coverage vector

measured 43 of 43 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-03T11:57:34.278440Z

measured 43 of 43 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+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

43 of 43 outbound references displayed

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External citation measurements

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Outbound references

Observation 9080e265-2574-48b4-b5f0-ff3736bea012 · outbound

This paper cites Deep Learning with Differential Privacy.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Deep Learning with Differential Privacy

Reference 1

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source=pdf_text observed=2026-08-03T11:57:34.018031Z digest=sha256:49e7bbff9ad0fafd7703efe4f03dcb368a0d12767ac963444add9d5b463ad007

Observation 821b71a1-c386-4249-86a0-ed6168e08682 · outbound

This paper cites Prio+: Privacy pre- serving aggregate statistics via boolean shares.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Prio+: Privacy pre- serving aggregate statistics via boolean shares

Reference 2

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Observation d551fea1-43c0-4341-8ed2-3f44b21fc14e · outbound

This paper cites Flchain: A blockchain for auditable federated learning with trust and incentive.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Flchain: A blockchain for auditable federated learning with trust and incentive

Reference 3

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Observation 2488c569-235b-480b-9118-a80f8c35aa98 · outbound

This paper cites Secure single-server aggregation with (poly) logarithmic overhead.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Secure single-server aggregation with (poly) logarithmic overhead

Reference 4

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Observation c8eab9fb-2a86-4858-8b30-6852df15a89c · outbound

This paper cites Asynchronous secure computation.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Asynchronous secure computation

Reference 5

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source=pdf_text observed=2026-08-03T11:57:34.110380Z digest=sha256:328bf3551d7f0f65af7bdf0740727a91d73e58a36d9924d4f2ce4360dce55e32

Observation 9a0e47e9-8781-4ef0-9a26-be047addec8e · outbound

This paper cites Fantastyc: Blockchain-based federated learning made secure and practical.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Fantastyc: Blockchain-based federated learning made secure and practical

Reference 6

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Observation 7df3e844-26fe-4d6e-a0e6-57e5c3453a96 · outbound

This paper cites Practical Secure Aggregation for Privacy-Preserving Machine Learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Practical Secure Aggregation for Privacy-Preserving Machine Learning

Reference 7

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Observation 918dddd8-5de7-492d-9366-d29dd5327a90 · outbound

This paper cites Lightweight, Maliciously Secure Verifiable Function Secret Sharing.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Lightweight, Maliciously Secure Verifiable Function Secret Sharing

Reference 8

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source=pdf_text observed=2026-08-03T11:57:34.124094Z digest=sha256:154e00c30102be9ed1e38c5319ba1dc615ec2bb8d6fc4cd3c320152fe27d9968

Observation 49c4d0f5-80ca-4f8d-aeac-c43f87038651 · outbound

This paper cites Practical byzantine fault tolerance.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Practical byzantine fault tolerance

Reference 9

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Observation 5969802c-61e2-4f75-aac1-337b2dff9c5e · outbound

This paper cites Homomorphic Secret Sharing with Veri- fiable Evaluation.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Homomorphic Secret Sharing with Veri- fiable Evaluation

Reference 10

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source=pdf_text observed=2026-08-03T11:57:34.132876Z digest=sha256:721cef09f9f96ee291b94f1ce94941f185aec53a0d10af9b181668bc94bd9182

Observation 6cbb4692-045d-4bb7-9285-e1e370535812 · outbound

This paper cites Prio: Private, robust, and scalable computation of aggre- gate statistics.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Prio: Private, robust, and scalable computation of aggre- gate statistics

Reference 11

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source=pdf_text observed=2026-08-03T11:57:34.137525Z digest=sha256:0682d3d6bb299436873937fe7d7e843535d7f67f0fc0425bfb0f8c700d8fe8fb

Observation 73671f2c-c006-470d-b76f-1b8d50024d8f · outbound

This paper cites Cryp- toNets: applying neural networks to encrypted data with high throughput and accuracy.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Cryp- toNets: applying neural networks to encrypted data with high throughput and accuracy

Reference 12

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source=pdf_text observed=2026-08-03T11:57:34.142983Z digest=sha256:a0ade995449b5a0f9df11810c6070c1b5be87c8fbda7287e9384a5457f9b12ae

Observation 64cbaa41-2e32-4844-a62f-0ce6829a20a8 · outbound

This paper cites BEAT: Asynchronous BFT Made Practical.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks BEAT: Asynchronous BFT Made Practical

Reference 13

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source=pdf_text observed=2026-08-03T11:57:34.147699Z digest=sha256:39a2f0ecd041ab09bb60aa27bfc58c4bbc6703f80a6947e72f38a116768ddab8

Observation 9a87cffe-11c0-4839-8069-9bcd2fbecd45 · outbound

This paper cites Differential Privacy.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Differential Privacy

Reference 14

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source=pdf_text observed=2026-08-03T11:57:34.151971Z digest=sha256:7c5cac8e23468389e2811c586e41820d3ab984ec61b923e161c755d3918e83aa

Observation 3436a4be-399c-4ed2-b3f8-e3b7ef1969da · outbound

This paper cites SEALion: a Framework for Neural Network Inference on Encrypted Data.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks SEALion: a Framework for Neural Network Inference on Encrypted Data

Reference 15

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source=pdf_text observed=2026-08-03T11:57:34.156533Z digest=sha256:43bad841f83636865f7f80c6d20ae79755e0547d2f33533f9c3f7c91fcd6fe90

Observation f2a77421-fcfb-4cf9-bbd2-1e2770f65f7e · outbound

This paper cites Impossibility of Distributed Consensus with One Faulty Process.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Impossibility of Distributed Consensus with One Faulty Process

Reference 16

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source=pdf_text observed=2026-08-03T11:57:34.161345Z digest=sha256:6363aa8a7069545e4aa479159e9f4a2d4acd40758dec9f59c0aa2bbe0a2c9a85

Observation ce364021-0b8a-4cf9-b95c-ec525c77f02c · outbound

This paper cites Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directions

Reference 17

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source=pdf_text observed=2026-08-03T11:57:34.165445Z digest=sha256:7a73030fb4bfdbac0eb563c933ed7a428d8ab13d6a5a444766f7fd0b875e34d0

Observation fd89c0dd-d32e-472c-9616-f5af4505b3a4 · outbound

This paper cites Differential privacy and byzantine resilience in sgd: Do they add up?.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Differential privacy and byzantine resilience in sgd: Do they add up?

Reference 18

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source=pdf_text observed=2026-08-03T11:57:34.170283Z digest=sha256:2ba5ceb64bd84f3685395eece1ef0001277100e8df47b3b927b86b3ec353b7d7

Observation 09577d2c-a471-4614-a1f1-cb20ea38420c · outbound

This paper cites V eri fl: Communication- efficient and fast verifiable aggregation for federated learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks V eri fl: Communication- efficient and fast verifiable aggregation for federated learning

Reference 19

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source=pdf_text observed=2026-08-03T11:57:34.174831Z digest=sha256:c4b002f25fb4c0c42ad9f789d0d5de3db609b0f74af79ebb98bd242d68776643

Observation 6824cb63-ce02-46d1-94f8-b84823a71d0f · outbound

This paper cites Multi-Agent eXperimenter (MAX).

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Multi-Agent eXperimenter (MAX)

Reference 20

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source=pdf_text observed=2026-08-03T11:57:34.179158Z digest=sha256:06a79a7f5f981bf6ef9becf027d71862f5cde1d10feac042e0ec331e810d1d00

Observation e7cc4f23-87c8-4869-84ec-fce0f720eb41 · outbound

This paper cites Cheater-identifiable homomorphic secret sharing for outsourcing computations.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Cheater-identifiable homomorphic secret sharing for outsourcing computations

Reference 21

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source=pdf_text observed=2026-08-03T11:57:34.183740Z digest=sha256:c41b6b7ce4a62b86caa5071b6a2422e30de82fe7e554bbf4b2fb59dd31e6b7d6

Observation ea769c71-026f-4b7f-b793-856d3a3c5ac0 · outbound

This paper cites An Enciphering Scheme Based on a Card Shuffle.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks An Enciphering Scheme Based on a Card Shuffle

Reference 22

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source=pdf_text observed=2026-08-03T11:57:34.187851Z digest=sha256:4c816331c7a84da4eb5982a41d63d3b44516080b0f7bbc298ec3dcc761c2f266

Observation e5561006-e577-4ed3-b2aa-c456872fec8e · outbound

This paper cites Constant-Size Commitments to Polynomials and Their Applications.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Constant-Size Commitments to Polynomials and Their Applications

Reference 23

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source=pdf_text observed=2026-08-03T11:57:34.191866Z digest=sha256:99356bca079bc8dd77f69121b2fde1c2e88ac465107cd20dc7538c91afb7cde9

Observation e44f81b8-4558-4a21-bff7-3bae0f3e6246 · outbound

This paper cites Blockchained on-device federated learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Blockchained on-device federated learning

Reference 24

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source=pdf_text observed=2026-08-03T11:57:34.195957Z digest=sha256:f51e8ec63b89b4eb5c3ea245d50fe895f8d191b4c2aff54449359a4aa4f79099

Observation 36803cdb-4b40-44bd-aef6-6be5065dfd13 · outbound

This paper cites Byzantine quorum systems.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Byzantine quorum systems

Reference 25

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source=pdf_text observed=2026-08-03T11:57:34.200134Z digest=sha256:8cb9db37607cc467acdfbf073aebf4bb86af0f833b8c04ab5ce65aae58fd82a6

Observation b52b442d-edfe-4a04-84fa-6ed18cc8b14b · outbound

This paper cites Communication- Efficient Learning of Deep Networks from Decentralized Data.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Communication- Efficient Learning of Deep Networks from Decentralized Data

Reference 26

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source=pdf_text observed=2026-08-03T11:57:34.204208Z digest=sha256:5f0354f34968dd572cd8e222bea902af3678fb00d0c7b8228fad610b97e44112

Observation 2ae3770c-24d6-4e69-bc5c-8a3d6fdf58fe · outbound

This paper cites Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning).

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning)

Reference 27

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source=pdf_text observed=2026-08-03T11:57:34.208640Z digest=sha256:43c171a5428b5d55755dfb798386763b3c70aa232e3ddbf5af074f0f03a155f8

Observation 29dd5c40-93ec-4a0c-b915-765cf0be324a · outbound

This paper cites Genuinely distributed byzantine machine learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Genuinely distributed byzantine machine learning

Reference 28

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source=pdf_text observed=2026-08-03T11:57:34.213413Z digest=sha256:58f633266af84b74d67a0337779726e2eef71dd75b048d55ea9ba8f1725ccb37

Observation be20a9af-b2fc-4a55-8dee-edf860077963 · outbound

This paper cites R´ enyi Differential Privacy.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks R´ enyi Differential Privacy

Reference 29

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source=pdf_text observed=2026-08-03T11:57:34.218060Z digest=sha256:7ae69c7937d4e6e8fef1c1bb0a228db93fb3fc39c8a6725791d090d76af721d1

Observation 8999a4d4-4f37-4f49-a5f6-2220ec6a3eab · outbound

This paper cites Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Non-Interactive and Information-Theoretic Secure Verifiable Secret Sharing

Reference 30

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source=pdf_text observed=2026-08-03T11:57:34.222759Z digest=sha256:f7c8b69db22ae5608dfce277e6fa4aa5209480cc1fb9ef9c612e58cca4f9d5e4

Observation 30c4c26c-85f1-4ecd-bf13-9eca35244396 · outbound

This paper cites Elsa: Secure aggregation for federated learning with malicious actors.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Elsa: Secure aggregation for federated learning with malicious actors

Reference 31

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source=pdf_text observed=2026-08-03T11:57:34.227006Z digest=sha256:e083af8e8ab3ea346565d394027a0755d143c1e002e65ac756a27b0cc8f1e543

Observation 0e6fbbcf-0418-4c72-8688-a2b9b7e9421f · outbound

This paper cites On lattices, learning with errors, random linear codes, and cryptography.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks On lattices, learning with errors, random linear codes, and cryptography

Reference 32

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source=pdf_text observed=2026-08-03T11:57:34.231098Z digest=sha256:d14aa078d40cc308e4e279af7cc56ddeaa1b9c1421e51b08446954b5e49ab872

Observation e3bdfcf0-ed95-413f-84ed-69acb3c8c359 · outbound

This paper cites An accurate, scalable and verifiable protocol for fed- erated differentially private averaging.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks An accurate, scalable and verifiable protocol for fed- erated differentially private averaging

Reference 33

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source=pdf_text observed=2026-08-03T11:57:34.235138Z digest=sha256:6c6df5fab0a1591d43ae58534d6e7214d86b38340c5d002d49d0e4eae34dd65e

Observation f0793a01-7608-4359-b172-b00365848a76 · outbound

This paper cites How to share a secret.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks How to share a secret

Reference 34

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source=pdf_text observed=2026-08-03T11:57:34.239926Z digest=sha256:a2a155fc2e3f87ef7a85ebd255bba6f9d5597899ce90de965b47d163797e68aa

Observation 078dc876-af87-4376-917f-731edac6260f · outbound

This paper cites Biscotti: A blockchain system for private and secure federated learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Biscotti: A blockchain system for private and secure federated learning

Reference 35

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source=pdf_text observed=2026-08-03T11:57:34.243977Z digest=sha256:28b191f726ed9726bba105f28c12fc275f928e7e73946ce60a8bc0acd5ad9746

Observation b723d079-d017-4c4a-9328-16d5755841db · outbound

This paper cites Practical threshold signatures.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Practical threshold signatures

Reference 36

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source=pdf_text observed=2026-08-03T11:57:34.248619Z digest=sha256:b2d9fa626fb0be935a952de2a695065102e5d3a61ac6764f27425354895dbfc3

Observation cd160e5c-8d71-452b-a494-04bb77b5be34 · outbound

This paper cites Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors

Reference 37

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source=pdf_text observed=2026-08-03T11:57:34.253246Z digest=sha256:4f63267673b78e166e751af413458b7788dbc60fbe8c2b62b2d46d4d3d2dec4a

Observation 174ea9b2-8b04-4466-b5b0-d2119731d335 · outbound

This paper cites A flexible and scalable malicious secure aggregation protocol for federated learning.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks A flexible and scalable malicious secure aggregation protocol for federated learning

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-03T11:57:34.257286Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:57:34.257286Z digest=sha256:3be19f7475af2ddb5f011731010fe7d50fd2ee5135cfaa0ac124488dc10cad44

Observation 94d0f93f-04a7-46ab-a3c0-a8a9c158d608 · outbound

This paper cites Sum It Up: Verifiable Additive Homomorphic Secret Sharing.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Sum It Up: Verifiable Additive Homomorphic Secret Sharing

Reference 39

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no resolver link, observed 2026-08-03T11:57:34.261491Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T11:57:34.261491Z digest=sha256:3e69936498453ba787462a882aca2b396e9be99a6e233b8ad95f6d7c5fdad48b

Observation 51d04516-2b37-45ee-8c32-edc784b20943 · outbound

This paper cites VerifyNet: Secure and verifiable federated learn- ing.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks VerifyNet: Secure and verifiable federated learn- ing

Reference 40

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unresolved
no resolver link, observed 2026-08-03T11:57:34.266414Z

Source-reported events for the cited work

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source=pdf_text observed=2026-08-03T11:57:34.266414Z digest=sha256:e72b864bad53fab8930125c3944d86b51fbfe63e8dbb7eefdfbf06dff6dfc1b9

Observation 2d359de4-37fc-4bb4-ab6d-1c13c4a323ab · outbound

This paper cites Privacy risk in machine learning: Ana- lyzing the connection to overfitting.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Privacy risk in machine learning: Ana- lyzing the connection to overfitting

Reference 41

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no resolver link, observed 2026-08-03T11:57:34.270370Z

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source=pdf_text observed=2026-08-03T11:57:34.270370Z digest=sha256:948e524c933a76064de13d488f4247f07ba413ab00a202bcd206ff58e012a47e

Observation 9c361cdf-6361-42b9-b66c-6d285d54bf1d · outbound

This paper cites See through gradients: Image batch recovery via gradinversion.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks See through gradients: Image batch recovery via gradinversion

Reference 42

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no resolver link, observed 2026-08-03T11:57:34.274568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:57:34.274568Z digest=sha256:ea33e2ac295ec8e9e8ac6851fe9f3836459033930b12b7900292100ff236e34c

Observation 8a295311-bdae-4a4f-8d28-1a6cb00d7957 · outbound

This paper cites Deep leakage from gradients.

Privacy-Preserving Federated Averaging with Byzantine Aggregators in Asynchronous Networks Deep leakage from gradients

Reference 43

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no resolver link, observed 2026-08-03T11:57:34.278440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:57:34.278440Z digest=sha256:e00f4b3d220fc57591bb0bbe0233405501e6306e8cc80aa2d97437991a57d737

Pith citing papers

No inbound Pith citation observations are available.