Paper Citation Record · LEDGER

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning

As of 19 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2506.16458.

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

pith.paper-citation-record.v1
2506.16458 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T19:31:57.712751Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+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

24 of 24 outbound references displayed

verified exact1
verified fuzzy18
unresolved5
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 978341f3-f43f-4473-83de-1994c2742b24 · outbound

This paper cites Towards federated learning at scale: System design,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Towards federated learning at scale: System design,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:58.002092Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation aff8baa0-f62a-4967-9bfb-62235df183de · outbound

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Advances and open problems in federated learning,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.992105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 2c9eb00e-5da5-4a76-bb06-2a5c574b37ae · outbound

This paper cites How to backdoor federated learning,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning How to backdoor federated learning,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.981901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ef452658-ebf8-4d31-919d-dfcf6edb5a02 · outbound

This paper cites Mitigating Backdoor Attacks in Federated Learning.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Mitigating Backdoor Attacks in Federated Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:57.634471Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 46a0a9f8-9c06-41dd-b8dd-4b29c9c08970 · outbound

This paper cites Jolliffe, Principal Component Analysis , 2nd ed.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Jolliffe, Principal Component Analysis , 2nd ed

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.971611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 92ddca30-49ef-485d-bc05-3e973de93ccf · outbound

This paper cites Gradient similarity-based defense against model poi- soning attacks in federated learning,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Gradient similarity-based defense against model poi- soning attacks in federated learning,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.960734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3c0277cb-5cc6-40e7-bf38-020d74251ed0 · outbound

This paper cites Elsa: Secure aggregation for federated learning with malicious actors,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Elsa: Secure aggregation for federated learning with malicious actors,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.949823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c76f5a4a-c54c-4dd3-8d74-fb873cf3f6e5 · outbound

This paper cites Challenges and future directions of secure federated learning: a survey,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Challenges and future directions of secure federated learning: a survey,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.938407Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bf2a8342-78a1-4759-9779-aee8bcbb1492 · outbound

This paper cites Learning to Detect Malicious Clients for Robust Federated Learning.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:57.653678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e15f6ef8-bf64-4489-a457-e1d69651cc2b · outbound

This paper cites Feddmc: Efficient and robust federated learning via detecting malicious clients,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Feddmc: Efficient and robust federated learning via detecting malicious clients,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.927397Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 377a0e40-df2c-407f-8249-372ff5ae5fb6 · outbound

This paper cites Hierarchical federated learning based anomaly de- tection using digital twins for smart healthcare,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Hierarchical federated learning based anomaly de- tection using digital twins for smart healthcare,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.915478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation afe13348-a01c-49ba-95f3-83e4e67f4a04 · outbound

This paper cites Hierarchical federated transfer learning and digital twin enhanced secure cooperative smart farming,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Hierarchical federated transfer learning and digital twin enhanced secure cooperative smart farming,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.904130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9cae6ddc-05d4-4df4-8abe-140e5f2bc192 · outbound

This paper cites Securing llm workloads with nist ai rmf in the internet of robotic things,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Securing llm workloads with nist ai rmf in the internet of robotic things,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.893367Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40cdcab9-c80f-4179-9cfc-e64611788270 · outbound

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Machine learning with adversaries: Byzantine tolerant gradient descent,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.881135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d8336108-b987-466b-8369-201376d385a8 · outbound

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Byzantine-robust distributed learning: Towards optimal statistical rates,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.869564Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9f3a7c93-9fc9-4b45-b9ef-0446823e738b · outbound

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning The limitations of federated learning in sybil settings,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.858137Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e7ecdea7-a19a-47b6-ba30-d7bb8326492c · outbound

This paper cites Flame: Taming backdoors in federated learning,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Flame: Taming backdoors in federated learning,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.846942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation a351f5eb-fbf8-497b-90b5-3252e75e04d3 · outbound

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:57.687863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 06551920-5115-4656-a02d-f7ddc884ba20 · outbound

This paper cites Differentially private pca in federated learning,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Differentially private pca in federated learning,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.835477Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation fcbf36af-0450-4503-8c7b-5fca9ee38b88 · outbound

This paper cites Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:57.696525Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ae6bc494-87a5-49c1-80fb-f2da8c316502 · outbound

This paper cites Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Federated Learning with Anomaly Detection via Gradient and Reconstruction Analysis

Reference 21

Resolution: verified exact; local_arxiv, observed 2026-08-15T19:31:57.758721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 513f89bc-ee0f-4d6e-9bcf-d31e4b5ccee9 · outbound

This paper cites Seaflame: Communication-efficient secure aggregation for federated learning against malicious entities,.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Seaflame: Communication-efficient secure aggregation for federated learning against malicious entities,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.823848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation eceed449-bdcf-4a27-80f5-4f0bb14327d1 · outbound

This paper cites Handwritten digits dataset (not in mnist),.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Handwritten digits dataset (not in mnist),

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T19:31:57.812259Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation f4581340-0c1b-47ab-a42b-b307deb59ca5 · outbound

This paper cites Data Poisoning Attacks Against Federated Learning Systems.

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning Data Poisoning Attacks Against Federated Learning Systems

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T19:31:57.712751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.