Paper Citation Record · LEDGER

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning

As of 9 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2604.03595.

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

pith.paper-citation-record.v1
2604.03595 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-13T12:53:49.802978Z

measured 20 of 20 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 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 exact0
verified fuzzy0
unresolved20
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation e0e64002-ba5d-45b9-abe7-c896c6271d79 · outbound

This paper cites Blindfl: Vertical federated machine learning without peeking into your data,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Blindfl: Vertical federated machine learning without peeking into your data,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ffc3413-e597-4f19-adee-3dbfc6528215 · outbound

This paper cites PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN

Reference 2

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d408d127-732b-4485-81a0-524e6c5b87b1 · outbound

This paper cites Asymmetrical Vertical Federated Learning.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Asymmetrical Vertical Federated Learning

Reference 3

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35f47f18-4c94-4374-b236-4c9a276b1f52 · outbound

This paper cites Splitfed: When federated learning meets split learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Splitfed: When federated learning meets split learning,

Reference 4

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a7585fb3-bbd4-4fa1-b359-9855aa028cf0 · outbound

This paper cites Split Learning for collaborative deep learning in healthcare.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Split Learning for collaborative deep learning in healthcare

Reference 5

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0dec129-483d-460a-a2c9-029cd74cd06a · outbound

This paper cites Split learning for health: Distributed deep learning without sharing raw patient data.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Split learning for health: Distributed deep learning without sharing raw patient data

Reference 6

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61f42bfa-1cd0-48cc-820e-13deb8e2227c · outbound

This paper cites Detailed comparison of communication efficiency of split learning and federated learning.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Detailed comparison of communication efficiency of split learning and federated learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40b3b043-b22a-4da6-8bfc-2a98cd0c06df · outbound

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

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Communication-efficient learning of deep networks from decentralized data,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c0e7640e-bcfb-452b-975f-d115090d26fd · outbound

This paper cites Split learning for distributed collaborative training of deep learning models in health informatics,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Split learning for distributed collaborative training of deep learning models in health informatics,

Reference 9

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8c21d3-a5d4-4d89-bab5-12ce2ae4cbbf · outbound

This paper cites Split learning optimized for the medical field: Reducing communication overhead,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Split learning optimized for the medical field: Reducing communication overhead,

Reference 10

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 849f3399-8fe8-46a3-807c-551138cf17ac · outbound

This paper cites Villain: Backdoor attacks against vertical split learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Villain: Backdoor attacks against vertical split learning,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ed21750-19b5-41f3-9735-cc7de1d2caf4 · outbound

This paper cites Backdoor attack against split neural network-based vertical federated learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Backdoor attack against split neural network-based vertical federated learning,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cff2cfc2-7848-4ea3-9a97-d34f2c7961f6 · outbound

This paper cites Badvfl: Backdoor attacks in vertical federated learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Badvfl: Backdoor attacks in vertical federated learning,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc53764c-81d4-467a-9560-2e73eaf33213 · outbound

This paper cites Securesplit: Mitigating backdoor attacks in split learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Securesplit: Mitigating backdoor attacks in split learning,

Reference 14

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70e2a79b-c448-4376-b46a-73798f84ce81 · outbound

This paper cites Vflip: A backdoor defense for vertical federated learning via identification and purification,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Vflip: A backdoor defense for vertical federated learning via identification and purification,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b5bdabc3-0510-4600-b10e-3ac38d39fa80 · outbound

This paper cites A data-driven approach to predict the success of bank telemarketing,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning A data-driven approach to predict the success of bank telemarketing,

Reference 16

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d72d2277-a0d0-4871-add7-f3ae0e216525 · outbound

This paper cites Deep learning with differential privacy,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Deep learning with differential privacy,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40e95068-54ee-4306-9321-77da44f63b6b · outbound

This paper cites Fine-pruning: Defending against backdooring attacks on deep neural networks,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Fine-pruning: Defending against backdooring attacks on deep neural networks,

Reference 18

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 99066e57-d9c9-4415-8171-7c67e48bb38d · outbound

This paper cites Adversarial neuron pruning purifies backdoored deep models,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Adversarial neuron pruning purifies backdoored deep models,

Reference 19

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7588d942-2a11-4aa2-9a58-cca8a9b617a5 · outbound

This paper cites Safesplit: A novel defense against client-side backdoor attacks in split learning,.

ProtoGuard-SL: Prototype Consistency Based Backdoor Defense for Vertical Split Learning Safesplit: A novel defense against client-side backdoor attacks in split learning,

Reference 20

Resolution: unresolved; no resolver link, observed 2026-07-13T12:53:49.802978Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.