Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-07T04:48:54.461270Z
Paper Citation Record · LEDGER
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity
As of 10 August 2026, this Paper Citation Record lists 3 of 3 outbound references and 0 inbound Pith citation observations for arXiv:2506.09824.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
3 of 3 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved2
- parse uncertain0
- malformed identifier1
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation e3b8f8b1-4ae2-4511-9162-1d6d695faeb0 · outbound
This paper cites FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping.
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping
Reference 900
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:48:54.297178Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation afa2a573-87d2-4084-8de3-7693c9730673 · outbound
This paper cites Do We Really Need to Design New Byzantine-robust Aggregation Rules?.
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity Do We Really Need to Design New Byzantine-robust Aggregation Rules?
Reference 2021
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T04:48:54.371332Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1dc86c89-17a7-41c3-bdc4-fc7f8c61e527 · outbound
This paper cites Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms.
A Weighted Loss Approach to Robust Federated Learning under Data Heterogeneity Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Reference 2023
- Resolution
- malformed identifier
- no resolver link, observed 2026-08-07T04:48:54.461270Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Pith citing papers
No inbound Pith citation observations are available.