Paper Citation Record · LEDGER

DROP: Poison Dilution via Knowledge Distillation for Federated Learning

As of 9 August 2026, this Paper Citation Record lists 67 of 67 outbound references and 1 inbound Pith citation observation for arXiv:2502.07011.

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

pith.paper-citation-record.v1
2502.07011 v2

Coverage vector

measured 67 of 67 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T14:06:54.760402Z

measured 68 of 68 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 2026-05-17T20:08:21.578954Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T20:10:10.948942Z

Reference resolution

67 of 67 outbound references displayed

verified exact6
verified fuzzy36
unresolved24
parse uncertain0
malformed identifier1
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 5d21d3c8-8d97-4a84-96f2-a386fc92e20a · outbound

This paper cites Scaling Laws for the Value of Individual Data Points in Machine Learning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Scaling Laws for the Value of Individual Data Points in Machine Learning

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.588630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b0ef749c-5879-4d78-bcb7-21297d397cd5 · outbound

This paper cites Realizing petabyte scale acoustic modeling,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Realizing petabyte scale acoustic modeling,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.978327Z

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.

Observation 6fecac3f-20fc-47b7-8d3a-493fd01fa3ad · outbound

This paper cites A survey on federated learning systems: Vision, hype and reality for data privacy and protection,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning A survey on federated learning systems: Vision, hype and reality for data privacy and protection,

Reference 3

Resolution: malformed identifier; arxiv_id_nonexistent, observed 2026-08-08T14:06:55.618600Z

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.

Observation f6ff712d-4d16-4351-8262-472ababa84d4 · outbound

This paper cites Sustainable AI: Environmental Implications, Challenges and Opportunities.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Sustainable AI: Environmental Implications, Challenges and Opportunities

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.603277Z

Source-reported events for the cited work

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Observation 3cce1aeb-b187-44b4-b6eb-f86908b6bcc3 · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Advances and open problems in federated learning,

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.606871Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bdcbd498-da37-4435-9e91-f174697f8720 · outbound

This paper cites Bad- nets: Evaluating backdooring attacks on deep neural networks,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Bad- nets: Evaluating backdooring attacks on deep neural networks,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.957737Z

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.

Observation 9f2ce43a-b719-4a2f-aa0e-9357f4cfaed5 · outbound

This paper cites Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Linkage on Security, Privacy and Fairness in Federated Learning: New Balances and New Perspectives

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.612339Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bea6b1d-401f-48d9-9cd8-3c08f97a9576 · outbound

This paper cites How To Backdoor Federated Learning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning How To Backdoor Federated Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.615421Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b722d2c-c493-4176-a6e6-cb98fb1c4680 · outbound

This paper cites Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Dataset security for machine learning: Data poisoning, backdoor attacks, and defenses,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.950145Z

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.

Observation aceaf075-185e-44ec-8a6a-7285bad9796f · outbound

This paper cites Trojaning attack on neural networks,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Trojaning attack on neural networks,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.942746Z

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.

Observation a35fed71-86e3-49f1-be29-bbb53908c39e · outbound

This paper cites Get rid of your trail: Remotely erasing backdoors in federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Get rid of your trail: Remotely erasing backdoors in federated learning,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.934635Z

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.

Observation e9492b6a-ba23-4b8e-81e4-a801cafc3f17 · outbound

This paper cites Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning

Reference 12

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:06:55.393499Z

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.

Observation f3ff8fa4-7288-4133-99cb-e9ff8809f13a · outbound

This paper cites A survey for federated learning evaluations: Goals and measures,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning A survey for federated learning evaluations: Goals and measures,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.920229Z

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.

Observation 38862f54-c623-4780-91f3-61139614e0c7 · outbound

This paper cites Certified robustness to label-flipping attacks via ran- domized smoothing,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Certified robustness to label-flipping attacks via ran- domized smoothing,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.903582Z

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.

Observation 6fc5d795-e882-41e8-8a47-9733eb1b63b5 · outbound

This paper cites Available: https://api.semanticscholar.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Available: https://api.semanticscholar

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.927816Z

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.

Observation 59854719-b6e1-4a07-b74e-581186da03d5 · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Local model poisoning attacks to Byzantine-Robust federated learning,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.895902Z

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.

Observation 3b7fd147-497a-4008-863d-c9f9ac0916de · outbound

This paper cites Data poisoning attacks against federated learning systems,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Data poisoning attacks against federated learning systems,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.888401Z

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.

Observation 856007b2-c68e-442e-ac62-3c0b4daced23 · outbound

This paper cites Byzantine- robust distributed learning: Towards optimal statistical rates,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Byzantine- robust distributed learning: Towards optimal statistical rates,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.880516Z

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.

Observation e4f05905-03ff-4fb7-a432-9d2cb1e9222f · outbound

This paper cites Poisoning Attacks against Support Vector Machines.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Poisoning Attacks against Support Vector Machines

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.640262Z

Source-reported events for the cited work

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Observation 826b5c54-d033-4417-a0bd-1e3b307a288a · outbound

This paper cites FLTrust: Byzantine-robust federated learning via trust bootstrap- ping,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning FLTrust: Byzantine-robust federated learning via trust bootstrap- ping,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.862500Z

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.

Observation e5cf528a-5361-42e1-929c-938b47fac33f · outbound

This paper cites Mitigating Sybils in Federated Learning Poisoning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Mitigating Sybils in Federated Learning Poisoning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.655391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8df0902a-51e9-4e8c-b9b1-0869ece79d83 · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Auror: defending against poisoning attacks in collaborative deep learning systems,

Reference 22

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T14:06:55.368023Z

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.

Observation a08000ee-8624-4da4-9031-a8f35c7e8dfa · outbound

This paper cites Machine learning with adversaries: Byzan- tine tolerant gradient descent,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Machine learning with adversaries: Byzan- tine tolerant gradient descent,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.872245Z

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.

Observation be6ddbe9-9f1d-4af8-a534-7da87fb0d74c · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning {FLAME}: Taming backdoors in federated learning,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.845596Z

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.

Observation 03c3464a-12d9-4bb2-a3bc-cab59c32e2c3 · outbound

This paper cites Flip: A provable defense framework for backdoor mitigation in federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Flip: A provable defense framework for backdoor mitigation in federated learning,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.838382Z

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.

Observation 25c7b020-77b2-4f32-a6bb-de13e2396f8c · outbound

This paper cites Mesas: Poisoning defense for federated learning resilient against adaptive attackers,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Mesas: Poisoning defense for federated learning resilient against adaptive attackers,

Reference 26

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T14:06:55.187798Z

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.

Observation fd3a0649-e205-4f59-8930-9d83fafb09c1 · outbound

This paper cites Flare: defending federated learning against model poisoning attacks via latent space representations,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Flare: defending federated learning against model poisoning attacks via latent space representations,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.853689Z

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.

Observation d5dc3831-6f26-4fcc-8867-1c8dc98ede76 · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.674949Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9404f0b-9860-4e78-9724-cc88c78f00ae · outbound

This paper cites Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Manipulating machine learning: Poisoning attacks and countermeasures for regression learning,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.819590Z

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.

Observation 6551f7df-e17e-45b7-9c85-c3260d307b73 · outbound

This paper cites Using machine teaching to identify optimal training-set attacks on machine learners,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Using machine teaching to identify optimal training-set attacks on machine learners,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.810318Z

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.

Observation 9f52a3d6-73e7-4636-875a-bdf4cfbd6851 · outbound

This paper cites On the pitfalls of security evaluation of robust federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning On the pitfalls of security evaluation of robust federated learning,

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.830945Z

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.

Observation 43b3aeb0-bf50-4f64-89a4-0ea0c6bdf964 · outbound

This paper cites Manipulating the byzantine: Optimizing model poisoning attacks and de- fenses for federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Manipulating the byzantine: Optimizing model poisoning attacks and de- fenses for federated learning,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.790107Z

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.

Observation e980a83a-b632-4571-897d-9929ab080dd2 · outbound

This paper cites Understanding black-box predictions via influence functions,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Understanding black-box predictions via influence functions,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.782114Z

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.

Observation 83636c31-5a6d-4e81-b682-22c4cb904486 · outbound

This paper cites Poison frogs! targeted clean-label poisoning attacks on neural networks,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Poison frogs! targeted clean-label poisoning attacks on neural networks,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.774199Z

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.

Observation 397ae3db-f6b6-4c14-a440-feb1a1824938 · outbound

This paper cites Is feature selection secure against training data poisoning?.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Is feature selection secure against training data poisoning?

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.800214Z

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.

Observation e81fd54d-ef12-4ace-9421-986f32ea6039 · outbound

This paper cites Attack of the tails: Yes, you really can backdoor federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Attack of the tails: Yes, you really can backdoor federated learning,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.758222Z

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.

Observation 5c97023e-e1df-4a73-845e-d9ea58657a0a · outbound

This paper cites Can You Really Backdoor Federated Learning?.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Can You Really Backdoor Federated Learning?

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.694452Z

Source-reported events for the cited work

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Observation 2b37bad6-76af-446e-87eb-e76e4bf22290 · outbound

This paper cites Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.697141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d4cf67d-d710-4e0f-9714-1cfb64954bc5 · outbound

This paper cites When does machine learning FAIL? generalized transferability for evasion and poisoning attacks,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning When does machine learning FAIL? generalized transferability for evasion and poisoning attacks,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.766687Z

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.

Observation 20b07886-4981-432a-8842-1edda5981cfa · outbound

This paper cites Modern hierarchical, agglomerative clustering algorithms.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Modern hierarchical, agglomerative clustering algorithms

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.702296Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b833d80e-878e-4ba8-9c62-0187ec00fdc1 · outbound

This paper cites Baybfed: Bayesian backdoor defense for federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Baybfed: Bayesian backdoor defense for federated learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.744750Z

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.

Observation 5be04ebd-9af1-4b89-84a7-c8d369ff07fe · outbound

This paper cites Density- based clustering based on hierarchical density estimates,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Density- based clustering based on hierarchical density estimates,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.736026Z

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.

Observation abcce5de-5b53-4c9e-8016-8872c1bcfb29 · outbound

This paper cites Robust aggregation for federated learning,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Robust aggregation for federated learning,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.700261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation be84c47d-0534-4371-9362-4c725bbd4a93 · outbound

This paper cites Maze: Data-free model stealing attack using zeroth-order gradi- ent estimation,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Maze: Data-free model stealing attack using zeroth-order gradi- ent estimation,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.727481Z

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.

Observation f394fc7f-f261-4287-8bd6-39c6c2f6c60d · outbound

This paper cites Distilling the knowledge in a neural network,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Distilling the knowledge in a neural network,

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.714494Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 988ae2ce-4311-4d8a-8a1c-2dd7e3cf33ce · outbound

This paper cites Unsupervised rep- resentation learning with deep convolutional generative adversarial networks,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Unsupervised rep- resentation learning with deep convolutional generative adversarial networks,

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.714999Z

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.

Observation 5670c8fc-b221-4ceb-940a-768fe00eff97 · outbound

This paper cites Hierarchical grouping to optimize an objective function,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Hierarchical grouping to optimize an objective function,

Reference 47

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-08T14:06:55.016375Z

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.

Observation 0be03bf0-6886-4586-be62-b21b4ce616a0 · outbound

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

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Learning multiple layers of features from tiny images,

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.721618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08065780-7133-49aa-8b58-2f51087ba4b7 · outbound

This paper cites CINIC-10 is not ImageNet or CIFAR-10.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning CINIC-10 is not ImageNet or CIFAR-10

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.723992Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 89461b38-cf91-41f9-aa6a-ec2c3617186b · outbound

This paper cites Imagenet: A large-scale hierarchical image database,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Imagenet: A large-scale hierarchical image database,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.726448Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0e6aa47b-4546-4901-ac15-9c64879cfe05 · outbound

This paper cites Data-free model extraction,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Data-free model extraction,

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.707946Z

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.

Observation 6de31b0a-a8ab-4dfe-ac44-dcdf2b1885d8 · outbound

This paper cites The mnist database of handwritten digit im- ages for machine learning research [best of the web],.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning The mnist database of handwritten digit im- ages for machine learning research [best of the web],

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.685843Z

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.

Observation bef857c7-20f5-402a-b7f4-7b2b15ba7675 · outbound

This paper cites Deep residual learning for image recognition,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Deep residual learning for image recognition,

Reference 53

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.733388Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 03ce907f-f1b6-477a-876c-da6ccb2f1f41 · outbound

This paper cites Federated Learning on Non-IID Data Silos: An Experimental Study.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Federated Learning on Non-IID Data Silos: An Experimental Study

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.735418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c84ef84a-bae0-4952-b7a8-9a35af078036 · outbound

This paper cites Emnist: an extension of mnist to handwritten letters,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Emnist: an extension of mnist to handwritten letters,

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.728700Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1280d9f8-267e-4640-90db-37f59a8894c9 · outbound

This paper cites Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning

Reference 56

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:06:54.794989Z

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.

Observation e4a6229f-f15d-4e6e-b958-e416a9937429 · outbound

This paper cites Robustness May Be at Odds with Accuracy.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Robustness May Be at Odds with Accuracy

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-08T14:06:54.743166Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2438a139-3a95-4f98-8042-6095b36d8372 · outbound

This paper cites Tutorial on large deviations for the binomial distribution,.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Tutorial on large deviations for the binomial distribution,

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.671467Z

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.

Observation a2cacd5c-6f04-4ba5-bbe3-863618265159 · outbound

This paper cites Neurotoxin: Durable Backdoors in Federated Learning.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Neurotoxin: Durable Backdoors in Federated Learning

Reference 59

Resolution: verified exact; local_arxiv, observed 2026-08-08T14:06:54.806320Z

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.

Observation b1673bf8-923f-4395-b175-359acee1d697 · outbound

This paper cites an unresolved cited work.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Unresolved cited work

Reference 63

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:06:55.663848Z

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.

Observation be8cf274-1654-4861-8506-c725dc95dc7b · outbound

This paper cites an unresolved cited work.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Unresolved cited work

Reference 64

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:06:55.657128Z

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.

Observation 5cf7798f-d248-4993-9dc9-777f8641cc2f · outbound

This paper cites While it does evaluate the method across different batch sizes, it lacks a detailed discussion of the broader local training setup.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning While it does evaluate the method across different batch sizes, it lacks a detailed discussion of the broader local training setup

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.648983Z

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.

Observation 516ad1e5-8a27-4371-b2c9-bd32225e25d7 · outbound

This paper cites an unresolved cited work.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Unresolved cited work

Reference 66

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:06:55.641325Z

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.

Observation 5bd795b7-0614-46c1-ae10-42419f3776e4 · outbound

This paper cites an unresolved cited work.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Unresolved cited work

Reference 67

Resolution: unresolved; raw_fallback, observed 2026-08-08T14:06:55.633135Z

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.

Observation 493daf92-1879-4abe-ab64-3a79df1f42ed · outbound

This paper cites Available: https://api.semanticscholar.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Available: https://api.semanticscholar

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.970846Z

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.

Observation 79ff3776-b50b-4721-ac70-f2ea5a62c1f4 · outbound

This paper cites Available: https://api.semanticscholar.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Available: https://api.semanticscholar

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.912483Z

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.

Observation f0295bf0-9d72-402b-bbdb-05f180709f3b · outbound

This paper cites Available: https://api.semanticscholar.

DROP: Poison Dilution via Knowledge Distillation for Federated Learning Available: https://api.semanticscholar

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T14:06:55.985338Z

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.

Pith citing papers

Observation 5ce3903b-102a-4ed8-a747-1901d1012487 · inbound

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning cites this paper.

FLARE: Adaptive Multi-Dimensional Reputation for Robust Client Reliability in Federated Learning DROP: Poison Dilution via Knowledge Distillation for Federated Learning

Reference 53

Resolution: verified exact; arxiv_id, observed 2026-05-17T20:10:10.950799Z

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.