Paper Citation Record · LEDGER

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

As of 14 August 2026, this Paper Citation Record lists 44 of 44 outbound references and 1 inbound Pith citation observation for arXiv:2507.07258.

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

pith.paper-citation-record.v1
2507.07258 v1

Coverage vector

measured 44 of 44 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:54:36.750893Z

measured 45 of 45 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-07T06:40:40.832348Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T10:16:28.603657Z

Reference resolution

44 of 44 outbound references displayed

verified exact0
verified fuzzy38
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7a048675-5d45-4d88-b73d-06897946611c · outbound

This paper cites Federated learning for malware detection in IoT devices,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Federated learning for malware detection in IoT devices,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.866101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6c802e08-0049-4a24-83da-63f84e001a01 · outbound

This paper cites Deep learning based xiot malware analysis: A comprehensive survey, taxonomy, and research challenges,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Deep learning based xiot malware analysis: A comprehensive survey, taxonomy, and research challenges,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.858853Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f9a0b7da-9ee1-439f-b208-c8f8156cb7bd · outbound

This paper cites IoT malware surges by 400% in 2023 with US being the most targeted country – Zscaler,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning IoT malware surges by 400% in 2023 with US being the most targeted country – Zscaler,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.850480Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0ea9ab62-69ca-43d7-9192-54ffdb8b31ba · outbound

This paper cites Machine learning algorithms and frameworks in ransomware detection,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Machine learning algorithms and frameworks in ransomware detection,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.843061Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d1fb718e-195d-4c36-9c8c-f0a7a06203e6 · outbound

This paper cites An adaptive federated learning scheme with differential privacy preserving,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning An adaptive federated learning scheme with differential privacy preserving,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.834742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a19973c9-34b9-49e1-a494-d62f21f40789 · outbound

This paper cites Graph representation feder- ated learning for malware detection in internet of health things,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Graph representation feder- ated learning for malware detection in internet of health things,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.817221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ac25ac3a-5bca-47dd-9004-0c86919a0801 · outbound

This paper cites Fs- real: Towards real-world cross-device federated learning,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Fs- real: Towards real-world cross-device federated learning,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.799190Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d62c7556-06cf-4be2-af91-99a743bf8e2b · outbound

This paper cites Sgde: Secure generative data exchange for cross-silo federated learning,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Sgde: Secure generative data exchange for cross-silo federated learning,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.778235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 3c35cf85-ee96-40ec-809e-034bf8771a04 · outbound

This paper cites Comparative analysis of federated learning, deep learning, and traditional machine learning techniques for iot malware detection,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Comparative analysis of federated learning, deep learning, and traditional machine learning techniques for iot malware detection,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.759332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fa28adc2-7c0a-4615-8daa-e46c30fd52db · outbound

This paper cites A horizontal federated learning approach to iot malware traffic detection: An empirical evaluation with n-baiot dataset,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning A horizontal federated learning approach to iot malware traffic detection: An empirical evaluation with n-baiot dataset,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.744995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ff9018a9-98de-4a4f-83da-8429f863d0b6 · outbound

This paper cites A federated learning based botnet detection method for industrial internet of things,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning A federated learning based botnet detection method for industrial internet of things,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.730045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ae3650ee-61c3-4c2f-8d53-0a1767d3be61 · outbound

This paper cites Distributed optimization for iot attack detection using federated learning and siberian tiger optimizer,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Distributed optimization for iot attack detection using federated learning and siberian tiger optimizer,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.712524Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 610f37be-8f8d-42c5-a276-0a37886d8ea7 · outbound

This paper cites A knowledge transfer- based semi-supervised federated learning for iot malware detection,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning A knowledge transfer- based semi-supervised federated learning for iot malware detection,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.697469Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8c0a13e5-ed13-47d3-93f7-1c1c36e48c3d · outbound

This paper cites Comprehensive android malware detection based on federated learning architecture,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Comprehensive android malware detection based on federated learning architecture,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.679398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e6aede1d-e27b-46fb-9daa-894cfe7b56ba · outbound

This paper cites Privacy-preserving malware detection in android-based iot devices through federated markov chains,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Privacy-preserving malware detection in android-based iot devices through federated markov chains,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.665338Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 23d587f6-3291-406c-9798-5153eef5dba2 · outbound

This paper cites Efficient and lightweight convolutional networks for iot malware detection: A federated learning approach,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Efficient and lightweight convolutional networks for iot malware detection: A federated learning approach,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.639766Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f1945040-5cad-4c4a-8ad9-3f09e62e4ce9 · outbound

This paper cites Federated learning with heterogeneous models for on-device malware detection in iot networks,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Federated learning with heterogeneous models for on-device malware detection in iot networks,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.603083Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f660eea0-f9c1-4865-8943-1e34cac0e37b · outbound

This paper cites Sim-fed: Secure iot malware detection model with federated learning,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Sim-fed: Secure iot malware detection model with federated learning,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.498470Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 2f04ecde-4779-41ad-b11d-f08d053d0de1 · outbound

This paper cites Federated learning-based ran- somware detection via indicators of compromise,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Federated learning-based ran- somware detection via indicators of compromise,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:42.211021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 0c62d44e-e4fa-4149-9480-4f2cc7f9050a · outbound

This paper cites Privacy-preserving federated learning approach for distributed malware attacks with in- termittent clients and image representation,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Privacy-preserving federated learning approach for distributed malware attacks with in- termittent clients and image representation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:41.968486Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 107b1a5b-7b38-45d7-a43b-edefa743a8f9 · outbound

This paper cites Client selection for federated learning with non-iid data in mobile edge computing,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Client selection for federated learning with non-iid data in mobile edge computing,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:41.673458Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation abd6463f-624b-4e9e-9ad9-4ed651a24510 · outbound

This paper cites Resource-efficient federated learning with non-iid data: An auction theoretic approach,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Resource-efficient federated learning with non-iid data: An auction theoretic approach,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:41.405289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1d5796f7-c63e-4bbb-a046-2cf81f963878 · outbound

This paper cites Fedsld: Federated learning with shared label distribu- tion for medical image classification,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Fedsld: Federated learning with shared label distribu- tion for medical image classification,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:41.108436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 91090d34-1e8a-44cf-8b4d-6f8d3012c434 · outbound

This paper cites Fednse: Optimal node selection for federated learning with non-iid data,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Fednse: Optimal node selection for federated learning with non-iid data,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:40.867264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1dc6ceeb-e246-48cd-8881-b552a6adb25b · outbound

This paper cites K-fl: Kalman filter- based clustering federated learning method,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning K-fl: Kalman filter- based clustering federated learning method,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:40.595356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 1aca4f7e-9689-40bb-990c-2e75522b4c87 · outbound

This paper cites Clustered federated multitask learning on non-iid data with enhanced privacy,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Clustered federated multitask learning on non-iid data with enhanced privacy,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:40.353383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d6e10f0f-b6e6-4507-a0f3-923974a2cb0e · outbound

This paper cites {IoTPOT}: analysing the rise of {IoT} compromises,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning {IoTPOT}: analysing the rise of {IoT} compromises,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:40.065554Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 4a5e0db4-9a0a-4b5d-bb36-513080ecee07 · outbound

This paper cites Bot-iot dataset - unsw research,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Bot-iot dataset - unsw research,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:39.843477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 99c0e968-b802-473f-872f-37f618c7e49f · outbound

This paper cites Iot-23 dataset: A labeled dataset for iot malware and benign traffic,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Iot-23 dataset: A labeled dataset for iot malware and benign traffic,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:39.605719Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c3fe6dc7-890a-4a59-8e4b-ab1067c0121b · outbound

This paper cites Drebin: Effective and explainable detection of android malware in your pocket.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Drebin: Effective and explainable detection of android malware in your pocket

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:39.331665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 504f8ac7-24b2-4fd2-adb6-263c0547ee71 · outbound

This paper cites Microsoft Malware Classification Challenge.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Microsoft Malware Classification Challenge

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:35.121164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c589db67-46e5-49d3-b329-943c0286ae1e · outbound

This paper cites Malware images: visualization and automatic classification,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Malware images: visualization and automatic classification,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:39.057360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f444fd12-6e5a-494a-9c6b-c21186fdf136 · outbound

This paper cites Dissecting android malware: Characterization and evolution,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Dissecting android malware: Characterization and evolution,

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:38.752948Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ac8ee19a-7a07-446d-9e7a-220fcee0cf8f · outbound

This paper cites A detailed analysis of the kdd cup 99 data set,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning A detailed analysis of the kdd cup 99 data set,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:38.491369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ef3a9b44-6eac-4e63-b69e-6a1fe6891828 · outbound

This paper cites Intrusion detection system for healthcare systems using medical and network data: A comparison study,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Intrusion detection system for healthcare systems using medical and network data: A comparison study,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:38.222286Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fe5b9911-7855-45d8-a13e-d6d2934ba7da · outbound

This paper cites N-baiot—network-based detection of iot botnet attacks using deep autoencoders,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning N-baiot—network-based detection of iot botnet attacks using deep autoencoders,

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:37.887642Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6a141563-bd76-4199-bc98-d923b9448c53 · outbound

This paper cites Iot-23: A labeled dataset with malicious and benign iot network traffic,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Iot-23: A labeled dataset with malicious and benign iot network traffic,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:37.613619Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 30030b59-854e-4308-b97b-1c167abffee8 · outbound

This paper cites Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Towards the development of realistic botnet dataset in the internet of things for network forensic analytics: Bot-iot dataset,

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:35.946693Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ff7862c9-f385-40cc-93d4-921dca466b0c · outbound

This paper cites Cross-Silo Federated Learning: Challenges and Opportunities.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Cross-Silo Federated Learning: Challenges and Opportunities

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:36.074239Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 68da6d5a-accd-4dad-8077-e265214ec429 · outbound

This paper cites Federated learning with non-iid data: A survey,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Federated learning with non-iid data: A survey,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:37.336811Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7801fbf1-f827-4901-8ef4-157ea40c17f4 · outbound

This paper cites Client specific dynamic aggregation for non-iid federated learning,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Client specific dynamic aggregation for non-iid federated learning,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T18:54:37.074543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation bf4571d5-0e26-4722-b198-c474c6375bef · outbound

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

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Communication-efficient learning of deep networks from decentralized data,

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:36.485708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98a50b2e-6813-4237-9b7b-e12890bf8cf3 · outbound

This paper cites Federated optimization in heterogeneous networks,.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning Federated optimization in heterogeneous networks,

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:36.600996Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa373523-ccb9-498a-a150-dfa50409866a · outbound

This paper cites A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points.

FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning A Non-parametric View of FedAvg and FedProx: Beyond Stationary Points

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-06T18:54:36.750893Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation a9d98713-529c-460a-bc57-14ff0c6e6b59 · inbound

Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning cites this paper.

Taming Noise-Induced Prototype Degradation for Privacy-Preserving Personalized Federated Fine-Tuning FedP3E: Privacy-Preserving Prototype Exchange for Non-IID IoT Malware Detection in Cross-Silo Federated Learning

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-12T10:16:28.605875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.