Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2411.02115.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T19:22:59.580672Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T15:40:18.959481Z
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 6758104e-f222-48f2-a0a4-d5a97755a3f8 · inbound
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach cites this paper.
Efficient Training of Large-Scale AI Models Through Federated Mixture-of-Experts: A System-Level Approach FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T19:22:59.580672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 15e700d4-e4e0-4846-93d3-38fd702f1e52 · inbound
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge cites this paper.
FFT-MoE: Efficient Federated Fine-Tuning for Foundation Models via Large-scale Sparse MoE under Heterogeneous Edge FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:22:25.548290Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 958cb295-b072-43e6-b362-06691e2e05f9 · inbound
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing cites this paper.
FLEX-MoE: Federated Mixture-of-Experts with Load-balanced Expert Assignment for Edge Computing FedMoE-DA: Federated Mixture of Experts via Domain Aware Fine-grained Aggregation
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T15:40:18.961643Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.