Paper Citation Record · LEDGER

FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2408.11304.

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

pith.paper-citation-record.v1
2408.11304 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:22:59.585433Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T00:09:15.183268Z

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 f7b5610e-13db-43b0-b8a6-7b3f3ba868db · inbound

A Survey on Foundation Models for Personalized Federated Intelligence cites this paper.

A Survey on Foundation Models for Personalized Federated Intelligence FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 211

Resolution: verified exact; arxiv_id, observed 2026-05-22T15:34:57.785819Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 39b2ebfe-28f6-4a87-8063-c68c533814d1 · 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: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-06T19:22:59.585433Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66c642d7-1089-4797-a0a6-f77d7fc6e08f · 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: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T16:22:25.483701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 40760460-1646-46d5-9892-75f8b2b62606 · 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: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-21T15:40:18.971273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation e3fce544-8ed3-44b0-a892-e8cc2787e7ab · inbound

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs cites this paper.

FedCoE: Bridging Generalization and Personalization via Federated Coordinated Dual-level MoEs FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-21T06:29:42.166560Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 804089ea-508b-4553-a10f-f088c75b2b43 · inbound

FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs cites this paper.

FoMoE: Breaking the Full-Replica Barrier with a Federation of MoEs FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 125

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:09:15.185886Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation ebaa96a5-4063-4e3c-bb71-47381bb7f7e7 · inbound

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning cites this paper.

Fisher-Routed Mixture of Experts for Federated Class-Incremental Learning FedMoE: Personalized Federated Learning via Heterogeneous Mixture of Experts

Reference 43

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T12:04:39.212273Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.