Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2201.11865.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T12:26:36.549684Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T22:26:13.913989Z
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 51167810-ee31-4001-a0e5-58599c76e59e · inbound
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning cites this paper.
SplitFedZip: Learned Compression for Data Transfer Reduction in Split-Federated Learning FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
Reference 29
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T12:26:36.549684Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 639be3b4-3d12-4212-abba-9b2ec1274773 · inbound
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations cites this paper.
A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations FedLite: A Scalable Approach for Federated Learning on Resource-constrained Clients
Reference 140
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T22:26:13.917523Z
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.