Paper Citation Record · LEDGER

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning

As of 17 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2508.10042.

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

pith.paper-citation-record.v1
2508.10042 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T21:37:00.094846Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+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

20 of 20 outbound references displayed

verified exact1
verified fuzzy19
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 38fe4712-027d-4150-bcdc-44712b4f4969 · outbound

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

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Data poisoning attacks against federated learning systems,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:04.486976Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0d17d4a9-8b1e-48e8-ba2e-73fbbed1119e · outbound

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

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:04.203062Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 199183bd-e155-4492-9337-65ef0d6f2cae · outbound

This paper cites Systematic analysis of label-flipping attacks against federated learning in collaborative intrusion detection systems,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Systematic analysis of label-flipping attacks against federated learning in collaborative intrusion detection systems,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:03.961116Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 9d59474f-34c0-4534-bfa2-c5475a945538 · outbound

This paper cites A survey on data poisoning attacks and defenses,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning A survey on data poisoning attacks and defenses,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:03.671631Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2dfec14e-3c66-4ab2-b099-c01912b3a175 · outbound

This paper cites Untargeted poisoning attack detection in federated learning via behavior attestational,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Untargeted poisoning attack detection in federated learning via behavior attestational,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:03.402090Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 0006a0f4-dca7-4e25-88c2-ba1f10b53a1b · outbound

This paper cites Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-08-05T21:37:00.281903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 782d004f-86d9-433c-b7fa-c2f93aece1d0 · outbound

This paper cites Zeno: Distributed stochastic gradient descent with suspicion-based fault- tolerance,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Zeno: Distributed stochastic gradient descent with suspicion-based fault- tolerance,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:03.175814Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 72e994fe-b8d4-439e-9f6c-1125eb864393 · outbound

This paper cites Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Deepchain: Auditable and privacy-preserving deep learning with blockchain-based incentive,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:02.863851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation df821e3a-54a9-47e8-af65-a85a9efde14c · outbound

This paper cites Blockchain assisted decentral- ized federated learning (blade-fl): Performance analysis and resource allocation,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Blockchain assisted decentral- ized federated learning (blade-fl): Performance analysis and resource allocation,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:02.599672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ffe34343-71e0-4062-846b-bb7f23556f10 · outbound

This paper cites Block- fla: Accountable federated learning via hybrid blockchain architecture,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Block- fla: Accountable federated learning via hybrid blockchain architecture,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:02.283002Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 700d1082-32ba-481f-b7e2-25d6600d2f99 · outbound

This paper cites Bafl: A blockchain-based asynchronous federated learning framework,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Bafl: A blockchain-based asynchronous federated learning framework,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:02.091333Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2992a1ab-c540-4e41-96a5-752235f86309 · outbound

This paper cites Hbfl: A hierarchical blockchain-based federated learn- ing framework for collaborative iot intrusion detection,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Hbfl: A hierarchical blockchain-based federated learn- ing framework for collaborative iot intrusion detection,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.897838Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ae9f2e3a-90cf-4cac-b6c2-90fa8add18d5 · outbound

This paper cites Recursive euclidean distance-based robust aggregation technique for federated learning,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Recursive euclidean distance-based robust aggregation technique for federated learning,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.712214Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ba33f9e7-d25f-4d41-9502-a9f0fbb68d3e · outbound

This paper cites Byzantine-robust federated learning via cosine similarity aggregation,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Byzantine-robust federated learning via cosine similarity aggregation,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.524092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f4a99a22-f9be-4e53-8bef-ce6997cee55b · outbound

This paper cites Mitigating data poisoning attacks on a federated learning-edge computing network,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Mitigating data poisoning attacks on a federated learning-edge computing network,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.377802Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2996bb46-bc9a-432e-b92e-d59fd24891b1 · outbound

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

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.180089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 80bece3b-484e-4305-9f32-437a5d002a0f · outbound

This paper cites Federated learning framework based on trimmed mean aggregation rules,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Federated learning framework based on trimmed mean aggregation rules,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:01.034485Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 2c7348ba-1d53-43cd-b831-0329de63f5c4 · outbound

This paper cites Robust aggregation for federated learning,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Robust aggregation for federated learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:00.862298Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation fdacd43b-40e2-4200-b483-4717287b5933 · outbound

This paper cites Trustworthy federated learning via blockchain,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Trustworthy federated learning via blockchain,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:00.667644Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1f23ed7d-7485-4a6e-8b82-fe861577549a · outbound

This paper cites Revisiting weighted aggregation in federated learning with neu- ral networks,.

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning Revisiting weighted aggregation in federated learning with neu- ral networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T21:37:00.470296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Pith citing papers

No inbound Pith citation observations are available.