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Paper Citation Record · LEDGER

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning

As of 9 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 1 inbound Pith citation observation for arXiv:2502.02038.

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

pith.paper-citation-record.v1
2502.02038 v2

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-09T13:39:53.302157Z

measured 51 of 51 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-08T20:04:30.942032Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-08T20:05:33.969916Z

Reference resolution

50 of 50 outbound references displayed

  • verified exact2
  • verified fuzzy32
  • unresolved16
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 44a1d6c1-d618-4b40-8033-04dbdf981104 · outbound

This paper cites A survey on federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning A survey on federated learning,

Reference 1

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.748169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.148980Z digest=sha256:5167cdbdcfd3c1519b5f87e16274371f61a1b72f8abadb3154fd1522bfd15a06

Observation 94cfe484-53f9-4cbe-97cc-a007e86e5b38 · outbound

This paper cites A Survey of Large Language Models.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning A Survey of Large Language Models

Reference 2

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unresolved
no resolver link, observed 2026-08-09T13:39:53.152195Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.152195Z digest=sha256:99dee3a55d77368a01ca2ada1a1dcb98a7d64e797fc25faab1e815603d1bcc45

Observation eda166f2-175d-46e3-a10b-f6427330fac7 · outbound

This paper cites Feashare: Feature sharing for computation correctness in edge preprocessing,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Feashare: Feature sharing for computation correctness in edge preprocessing,

Reference 3

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.738914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.155272Z digest=sha256:124cd781288b4f57a4bf64eb02d861ccef554f98a9c53588537c7e81a664f28a

Observation 147d1db6-7f69-4cbf-90b2-77bdeaa171d1 · outbound

This paper cites Semi-asynchronous online federated crowdsourcing,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Semi-asynchronous online federated crowdsourcing,

Reference 4

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.729508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.157943Z digest=sha256:e53c78cf63300bc5e63a1eeac97afd814c3a231ddd2dc6481a6b23ae44b568ac

Observation aa1fa5bd-1942-4f63-aec7-198f29580a1e · outbound

This paper cites Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning,

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.161276Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.161276Z digest=sha256:4adaff7a7525550f54550e9c0cab90c80aa2308a64caf586e9ec018d0a4c9845

Observation f5071fd5-38ea-4e00-89f5-5ae4fe273768 · outbound

This paper cites Label-only model inversion attacks via knowledge transfer,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Label-only model inversion attacks via knowledge transfer,

Reference 6

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.714935Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.164508Z digest=sha256:e42e6aed37f9797b459dd58c577d1dcc8ded1cb425dcbcbf2e268abbefb610a6

Observation e92281b5-7f5c-4007-b551-29976588869a · outbound

This paper cites Agic: Approximate gradient inversion attack on federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Agic: Approximate gradient inversion attack on federated learning,

Reference 7

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.706303Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.167695Z digest=sha256:cb8494ee3cdf8d13dd55ecc81a7667f3be8423b6c35f61d71bb0678daa9ff7b9

Observation 9ddb1ec7-679e-4479-869d-13cecdf3f2ee · outbound

This paper cites Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Data and model poisoning backdoor attacks on wireless federated learning, and the defense mechanisms: A comprehensive survey,

Reference 8

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.697605Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.170695Z digest=sha256:8e7e44148b6dbffee282dbda7cae20de5629a6b5e6ecd178fa4b93b2913784aa

Observation 422d9d55-98bf-4508-9a36-022cd495140b · outbound

This paper cites Mpaf: Model poisoning attacks to federated learning based on fake clients,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Mpaf: Model poisoning attacks to federated learning based on fake clients,

Reference 9

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.688005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.173572Z digest=sha256:a3785905041b1f8a8b7f1f42e61baa737adb6c0df85e7c30180b5b62f45370fa

Observation 87d37ef5-2c14-42c1-a431-1bd02df50c1d · outbound

This paper cites Safelearn: Secure aggregation for private federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Safelearn: Secure aggregation for private federated learning,

Reference 10

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.680130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.176778Z digest=sha256:ab46247d34e09abf6c78933db1dd2e51b5fb65e2ec2393ac9264c6f5857227ee

Observation 244ced5a-1953-4de1-9a9f-bb90f79e3cc7 · outbound

This paper cites Efficient and privacy-preserving feature importance-based vertical federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Efficient and privacy-preserving feature importance-based vertical federated learning,

Reference 11

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.672155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.179705Z digest=sha256:fd583baa5454c80c1ec2f40ac9defbad20f50a70bc3a880cae24e08e29f89208

Observation d0b857fa-4c49-431a-a718-e01ff5df0202 · outbound

This paper cites Ldp-fed: Federated learning with local differential privacy,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Ldp-fed: Federated learning with local differential privacy,

Reference 12

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.663534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.182896Z digest=sha256:1630b9e19a09859330e3adac4c62059add352617a0d50efc64f349187d3aab87

Observation 3815d90d-a1c3-4c9e-841f-0ae8f541c39e · outbound

This paper cites Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Random Gradient Masking as a Defensive Measure to Deep Leakage in Federated Learning

Reference 13

Resolution
verified exact
local_arxiv, observed 2026-08-09T13:39:53.418788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.185971Z digest=sha256:49a07f73756f15d46a14175be660648ccd44fe5d696fb722e1a0cafbee039263

Observation f450e2d8-0272-4cce-9663-eec143572ac3 · outbound

This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.189793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.189793Z digest=sha256:2dfa0bca6fbdb294ec5ba4cadfb0225950c720c2e42a97e3de9ed72d2d1e935b

Observation 825866a2-fb3b-4b8f-b5cd-e0d5b9067a98 · outbound

This paper cites Defending against back- doors in federated learning with robust learning rate,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Defending against back- doors in federated learning with robust learning rate,

Reference 15

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.654610Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.193020Z digest=sha256:8df9f1cc476e2e60fc45cd15b39a66270d11db50ac51f5300997c003dfca6a4b

Observation 37cc5747-6be7-4449-8e57-9e9157af9deb · outbound

This paper cites Poisoning with cerberus: Stealthy and colluded backdoor attack against federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Poisoning with cerberus: Stealthy and colluded backdoor attack against federated learning,

Reference 16

Resolution
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raw_fallback, observed 2026-08-09T13:39:53.645714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.195894Z digest=sha256:86e3c401087793de4763cf04e524d57ae577ac591b4855f9a5173a318f6e61c4

Observation 2b93fff7-9ddd-4fe7-9d6e-e701b7277324 · outbound

This paper cites Roseagg: Robust defense against targeted collusion attacks in federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Roseagg: Robust defense against targeted collusion attacks in federated learning,

Reference 17

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.637005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.199182Z digest=sha256:b972c82c50753f26d0f4a1105d934d68d5096ccc4ac003fdad490019a7fb75fe

Observation fe182978-2f4a-419e-a58f-75a417152daa · outbound

This paper cites Eiffel: Ensuring integrity for federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Eiffel: Ensuring integrity for federated learning,

Reference 18

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.627450Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.203151Z digest=sha256:e97daa99099c5f01054325a7ea4a2dde473f7f957a6d7ea6dff4cb5d48fb64c4

Observation e3e54d4e-1f00-4109-b7fc-70a878836c39 · outbound

This paper cites A survey on federated unlearning: Challenges, methods, and future directions,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning A survey on federated unlearning: Challenges, methods, and future directions,

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.206281Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.206281Z digest=sha256:bbc0ab2b3eb187ef8a2ac2e5a947663e68c548ec56ddd2ee3472a5c93a8c2ecc

Observation 8b7c8056-6778-4c36-a3dd-95a24bc860ca · outbound

This paper cites Detection and incentive: A tampering detection mechanism for object detection in edge computing,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Detection and incentive: A tampering detection mechanism for object detection in edge computing,

Reference 20

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.613082Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.209236Z digest=sha256:83947bd75f3d7ac7284196e75316bd6f7f0df9e34650546f1d6c3d42850e378c

Observation a7585880-315c-4858-bf56-b723a7953723 · outbound

This paper cites Heterogeneous federated knowledge graph embedding learning and unlearning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Heterogeneous federated knowledge graph embedding learning and unlearning,

Reference 21

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.603506Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.212345Z digest=sha256:e681115e3b76106613180d1a75f2f2bbff691ee6532da0d108b5b28f0723f018

Observation f45f999f-c881-4a27-9967-5aa3f19a4ad6 · outbound

This paper cites Revfrf: Enabling cross-domain random forest training with revocable federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Revfrf: Enabling cross-domain random forest training with revocable federated learning,

Reference 22

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.594296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.215267Z digest=sha256:8c9118b55a701a48d7df5d9481ab50ba4dc1d533ec263d2356a8fa072c5d4762

Observation 0fdcdbb5-7cfb-4703-b06b-36673febb1b7 · outbound

This paper cites Verifi: Towards verifiable federated unlearning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Verifi: Towards verifiable federated unlearning,

Reference 23

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.584158Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.218165Z digest=sha256:d6917977e4e7808cb45e9ad62719d3132958281bfe9ecba3b2de6f5929ae7226

Observation c31cac37-95fc-4237-ac23-9998ffd8eb59 · outbound

This paper cites Federated Unlearning: How to Efficiently Erase a Client in FL?.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Federated Unlearning: How to Efficiently Erase a Client in FL?

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.221771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.221771Z digest=sha256:b9f3af6a98fb1b74c094e817ae4a3f85f4eaa2884b351440dc289bf365f9a0d0

Observation 1d97fca1-4e03-402f-8d76-a22ff196b198 · outbound

This paper cites A blockchain-based shamir’s threshold cryptography scheme for data protection in industrial internet of things settings,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning A blockchain-based shamir’s threshold cryptography scheme for data protection in industrial internet of things settings,

Reference 25

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.573907Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.225880Z digest=sha256:90382628720632147850520e2c2d0d5e88fb82791ed3c042045ba60a62a6663a

Observation d3235944-4466-4987-9aa4-f36816746acd · outbound

This paper cites Edge computing: Vision and chal- lenges,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Edge computing: Vision and chal- lenges,

Reference 26

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.564463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.228903Z digest=sha256:b515f3c6c9b7376fa4ef554ce723102fbd48a43a4d0118d1dbd08e11f9dcb12e

Observation 32aaf10c-787e-42db-b1ad-9c3313366f92 · outbound

This paper cites Gradient-based learning applied to document recognition,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Gradient-based learning applied to document recognition,

Reference 27

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.231866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.231866Z digest=sha256:c68b10b4b317ea265272e7f9696612c8bec76a0dc9c0ae3b699306dd9834930e

Observation 9e06c695-7148-451c-93d8-03b96d16dd19 · outbound

This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.235042Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.235042Z digest=sha256:37e42124edbf3824bab92b877037525b80445417555ae1e2b7ac6891688dd312

Observation 2f32a9d5-2fa0-42c3-a06b-ba5d3b94adf8 · outbound

This paper cites Emnist: Extending mnist to handwritten letters,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Emnist: Extending mnist to handwritten letters,

Reference 29

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.550596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.238811Z digest=sha256:11bf6085aa5f589587427e5e883aac0b35aab8e827794b18e46532141f016134

Observation 94266a61-c035-40b0-b27c-04e991619f07 · outbound

This paper cites Learning multiple layers of features from tiny images,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Learning multiple layers of features from tiny images,

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.241180Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.241180Z digest=sha256:1e0b721ebb3161cf7c67b9b1f7d30047b03a828fa6d6e464033dea8e21e9a651

Observation 640c4231-f27d-4ee4-96f8-91f69b484c5e · outbound

This paper cites {FLAME}: Taming backdoors in federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning {FLAME}: Taming backdoors in federated learning,

Reference 31

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.538179Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.243532Z digest=sha256:f7b521f81b544f5bf2c2f242f00fc023e6963bf409908c18c7cd708c8bdf2b95

Observation af90d6af-0faa-477f-9619-60b2859f08d6 · outbound

This paper cites Ppfl: Privacy-preserving federated learning with trusted execution environments,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Ppfl: Privacy-preserving federated learning with trusted execution environments,

Reference 32

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.530828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.245849Z digest=sha256:c5cf9299303c2fd61c9d040c850186a06a9a2c722d9e53701304a33235d49840

Observation 0faea231-f9bc-4c8f-bd48-ee045a11eaa8 · outbound

This paper cites BayBFed: Bayesian Backdoor Defense for Federated Learning.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning BayBFed: Bayesian Backdoor Defense for Federated Learning

Reference 33

Resolution
verified exact
local_arxiv, observed 2026-08-09T13:39:53.382040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.248088Z digest=sha256:882eec23d4ac408a2977ca89ecfb772ec9a8e9339a031dbfd4be708907b99b2d

Observation 7b98ff38-5947-4d15-9d31-e011531a041d · outbound

This paper cites Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-09T13:39:53.250759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.250759Z digest=sha256:a815d211b8c9a38dcf8002654784712f5385ca129b070b758372e82cdb51bfb0

Observation 799f3f3c-25f5-4e4c-8f5e-3de7ea6bf8ae · outbound

This paper cites Federated Learning: Strategies for Improving Communication Efficiency.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Federated Learning: Strategies for Improving Communication Efficiency

Reference 35

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no resolver link, observed 2026-08-09T13:39:53.253277Z

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source=pdf_text observed=2026-08-09T13:39:53.253277Z digest=sha256:bf6700de33acc9cf1df6626f35b17a04f0f6366160587188178c6eb5a0c538b7

Observation 8bc750b7-e6ab-433c-bcab-7451645beaf5 · outbound

This paper cites Local model poisoning attacks to {Byzantine-Robust} federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Local model poisoning attacks to {Byzantine-Robust} federated learning,

Reference 36

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no resolver link, observed 2026-08-09T13:39:53.256300Z

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source=pdf_text observed=2026-08-09T13:39:53.256300Z digest=sha256:8fa46e9447caf5a7453c1088b215515028647299d34d55bac3b67dfb2833e92e

Observation 74380fa4-5c66-4ca6-81b0-91c8acf4561e · outbound

This paper cites DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning DeepSight: Mitigating Backdoor Attacks in Federated Learning Through Deep Model Inspection

Reference 37

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no resolver link, observed 2026-08-09T13:39:53.259427Z

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source=pdf_text observed=2026-08-09T13:39:53.259427Z digest=sha256:3f6a2d9aab50e80fc31ab403a6164b3f6eec55a6fcb2a4f2618e335516ddfc09

Observation b97af514-32cc-46a7-801a-83de5dd2d1bc · outbound

This paper cites FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning Attacks in Federated Learning

Reference 38

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source=pdf_text observed=2026-08-09T13:39:53.262605Z digest=sha256:b87defea2bcdaf074001ff971e7ecfa074243cee752b4bcba742fe172142b259

Observation 74c4c16b-5312-4bac-85a9-309f66639d8a · outbound

This paper cites Auror: Defending against poisoning attacks in collaborative deep learning systems,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Auror: Defending against poisoning attacks in collaborative deep learning systems,

Reference 39

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.516581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.266812Z digest=sha256:e0fcb62f40c9a9f6083b02a07bfb32e6f22fdd50157a357d3dfb52d6d0eb2eff

Observation f3f4cb34-432f-4fba-bacc-3bf1ddb3a1e4 · outbound

This paper cites Towards deep learning models resistant to adversarial attacks,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Towards deep learning models resistant to adversarial attacks,

Reference 40

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.508147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.269899Z digest=sha256:afd82e81b9d485ab350c23e083b5cda2b5516607e64b87992145680d403cd5ab

Observation e6f34589-aab0-4634-ae1d-4b915f76f4e3 · outbound

This paper cites Deep Leakage from Gradients.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Deep Leakage from Gradients

Reference 41

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no resolver link, observed 2026-08-09T13:39:53.272927Z

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source=pdf_text observed=2026-08-09T13:39:53.272927Z digest=sha256:bbd57d96f822e68a2c2802487167f59c03eaaa5de777efa221a8838f225af97f

Observation 35232b46-0e60-4562-80e3-e9651959291d · outbound

This paper cites Fedrecover: Recovering from poisoning attacks in federated learning using historical information,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Fedrecover: Recovering from poisoning attacks in federated learning using historical information,

Reference 42

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.499120Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.276008Z digest=sha256:b0aae981b35373dcf98b85d4764b3ea69c1fbf267fbb6e7735a2044318103dd9

Observation a3d0899b-dc8c-49f2-a409-d8d70aa6bfe0 · outbound

This paper cites Flairs: Fpga-accelerated inference-resistant & secure federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Flairs: Fpga-accelerated inference-resistant & secure federated learning,

Reference 43

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.490210Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.279286Z digest=sha256:822427980307cd5a12f63bec04f7fc6ac222f502549221047b8247973f12bef7

Observation adc923ea-002e-44b4-be2f-1c86d7cc0ef1 · outbound

This paper cites Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Toward robustness and privacy in federated learning: Experimenting with local and central differential privacy,

Reference 44

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.480725Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.282136Z digest=sha256:872f95d05c3c3e5fcb66e5061cfffd775f2517ba344f4e08281a617b6d3b5896

Observation 98a6671f-97f9-41e5-8e1d-3421371176df · outbound

This paper cites The limitations of federated learning in sybil settings,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning The limitations of federated learning in sybil settings,

Reference 45

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no resolver link, observed 2026-08-09T13:39:53.285645Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T13:39:53.285645Z digest=sha256:7165c97af4d9a1fd319b7c00168a3cf18884e52cfa539d6bbc81408db2e58961

Observation e869d33e-a05e-4a9e-9839-867e876158c3 · outbound

This paper cites Baffle: Backdoor de- tection via feedback-based federated learning,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Baffle: Backdoor de- tection via feedback-based federated learning,

Reference 46

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verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.466295Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.288612Z digest=sha256:a3258512882a8e49e5a548d2ab97f2c9d18283e7c1e7d822357c04df37f6a524

Observation edefd3f2-d019-4d57-826e-f44aa14f6199 · outbound

This paper cites Learning Differentially Private Recurrent Language Models.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Learning Differentially Private Recurrent Language Models

Reference 47

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Source-reported events for the cited work

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source=pdf_text observed=2026-08-09T13:39:53.291859Z digest=sha256:9191beccfcbabe6a0017917db37d5f98b7be8972117f6645be97e06eaae69666

Observation 751cca3c-4e16-4de5-bffd-abffcc0c02c8 · outbound

This paper cites Machine learning with adversaries: byzantine tolerant gradient descent,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Machine learning with adversaries: byzantine tolerant gradient descent,

Reference 48

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.457121Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.295302Z digest=sha256:dee2f01583a39f10d9187d59fa024df870e81177c581a24a2ee9ce37cb5eb3ad

Observation b3befb7b-0d70-40f6-9379-db33c72fdb46 · outbound

This paper cites Eluding secure aggregation in federated learning via model inconsistency,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Eluding secure aggregation in federated learning via model inconsistency,

Reference 49

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.447865Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.298993Z digest=sha256:9d3f4cf7032a2cb1c2da0875616bd66d8e7712c0ac947f749abc505d1d3c47d7

Observation 11c94e19-5a6e-4bea-9694-7c19d278cf5f · outbound

This paper cites Holodeck: Language guided generation of 3d embodied ai environments,.

SMTFL: Secure Model Training to Untrusted Participants in Federated Learning Holodeck: Language guided generation of 3d embodied ai environments,

Reference 50

Resolution
verified fuzzy
raw_fallback, observed 2026-08-09T13:39:53.437579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-09T13:39:53.302157Z digest=sha256:90d064ce6a3e9388b3c30cfaa59168eeb0c77352dc91c4c3f08543cf4e38122c

Pith citing papers

Observation 117a77f1-5897-48bd-b7c1-9b7442ca7793 · inbound

SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation cites this paper.

SearchEyes: Towards Frontier Multimodal Deep Search Intelligence via Search World Simulation SMTFL: Secure Model Training to Untrusted Participants in Federated Learning

Reference 58

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metadata mismatch
local_arxiv, observed 2026-07-08T20:05:33.971190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-07-08T20:04:30.942032Z digest=sha256:6037100a2d6385943af9b7dee94cffe18ac1fcd78a342c146e876d7ece51eabf