Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Ensemble Distillation for Robust Model Fusion in Federated Learning
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2006.07242.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T01:42:10.329196Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:47:18.798169Z
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 2757ddd9-10c0-400e-9360-bd762bffcac4 · inbound
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning cites this paper.
FedQHD: Closed-Form Function-Space Federated Reinforcement Learning Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 7
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-29T13:33:28.492004Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 278d2520-beaa-4500-b853-761f6614d6fa · inbound
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning cites this paper.
HASA: Subnet Allocation for Compute-Constrained Model-Heterogeneous Federated Learning Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 25
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-06-28T19:32:34.804612Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 0032fa71-f5b8-4321-be02-fce3b25e4fa4 · inbound
TallyTrain: Communication-Efficient Federated Distillation cites this paper.
TallyTrain: Communication-Efficient Federated Distillation Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 22
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:47:18.799610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation ff97ef09-7bf0-470d-ab93-433abd82e2b4 · inbound
Federated Lightweight Fine-Tuning cites this paper.
Federated Lightweight Fine-Tuning Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 19
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T17:36:04.983472Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8734924d-69e4-4388-85b5-3d43920324a3 · inbound
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-07-31T22:47:13.804563Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 20a33443-957e-48ff-b339-d8fd6e51dba9 · inbound
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement cites this paper.
TriShield: Zero-Utility-Loss Defense Against Privacy Backdoors in Federated Language Model Fine-Tuning via Orthogonal Gradient Projection and Optimizer State Entanglement Ensemble Distillation for Robust Model Fusion in Federated Learning
Reference 15
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T01:42:10.329196Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.