Paper Citation Record · LEDGER

Learning to Detect Malicious Clients for Robust Federated Learning

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2002.00211.

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

pith.paper-citation-record.v1
2002.00211 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:20:48.007440Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T23:13:37.023261Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bd39de33-7b71-448b-9cd6-fa4b644d3007 · inbound

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning cites this paper.

BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T23:13:37.026029Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 9af8c66f-9d6a-43fa-a9dc-69ac640f999c · inbound

FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning cites this paper.

FL-CLEANER: byzantine and backdoor defense by CLustering Errors of Activation maps in Non-iid fedErated leaRning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-10T17:34:41.771190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5aa0e0bc-7a58-46fc-915d-74a6eee969fd · inbound

Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism cites this paper.

Heterogeneous Federated Learning Systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism Learning to Detect Malicious Clients for Robust Federated Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T17:33:58.345141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8e938a50-8864-4d08-abe0-d21e15c32c74 · inbound

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning cites this paper.

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-09T19:57:50.369406Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 513c7ee6-8043-4b46-b612-021a5c621aae · inbound

Decoding FL Defenses: Systemization, Pitfalls, and Remedies cites this paper.

Decoding FL Defenses: Systemization, Pitfalls, and Remedies Learning to Detect Malicious Clients for Robust Federated Learning

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-09T14:12:23.240837Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 662c3bd4-b52a-457d-945c-3521099037d5 · inbound

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients cites this paper.

FL-PLAS: Federated Learning with Partial Layer Aggregation for Backdoor Defense Against High-Ratio Malicious Clients Learning to Detect Malicious Clients for Robust Federated Learning

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-15T20:46:59.220971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning cites this paper.

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 60fad88a-e755-47fc-8599-b6fc0005eb77 · inbound

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer cites this paper.

Measuring and Predicting Where and When Pathologists Focus their Visual Attention while Grading Whole Slide Images of Cancer Learning to Detect Malicious Clients for Robust Federated Learning

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-06T05:33:40.068366Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4bdafc3c-fe5c-4bc3-9770-019cd40190f2 · inbound

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks cites this paper.

FLAegis: A Two-Layer Defense Framework for Federated Learning Against Poisoning Attacks Learning to Detect Malicious Clients for Robust Federated Learning

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-05T16:20:23.334034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f37acc8-4b67-4fae-8c2c-f64b28fa6bd1 · inbound

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats cites this paper.

Enabling Trustworthy Federated Learning via Remote Attestation for Mitigating Byzantine Threats Learning to Detect Malicious Clients for Robust Federated Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-05T13:28:47.141282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bb8813a6-ddb6-4e23-979f-87b8378bd672 · inbound

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning cites this paper.

DFedReweighting: A Unified Framework for Objective-Oriented Reweighting in Decentralized Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-05-16T22:38:37.945553Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation dfc6b3fa-3bd2-4c78-bd4c-0faf85e77cb1 · inbound

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning cites this paper.

Beyond Parameter Space: NTK-Guided Personalized Aggregation for Robust Federated Learning Learning to Detect Malicious Clients for Robust Federated Learning

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-16T00:20:48.007440Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.