Paper Citation Record · LEDGER

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning

As of 21 August 2026, this Paper Citation Record lists 32 of 32 outbound references and 0 inbound Pith citation observations for arXiv:2504.21775.

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

pith.paper-citation-record.v1
2504.21775 v1

Coverage vector

measured 32 of 32 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:01:34.421649Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

32 of 32 outbound references displayed

verified exact1
verified fuzzy23
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3b22f01a-bb9c-4fb3-8a2e-3280382dd913 · outbound

This paper cites ProPublica's COMPAS Data Revisited.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning ProPublica's COMPAS Data Revisited

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-08-16T05:01:34.561459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b17ca98a-009c-4db2-a187-55d0c82b65ba · outbound

This paper cites (30) describes the convergence relationship between the hypernet and the communicated model at each round.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning (30) describes the convergence relationship between the hypernet and the communicated model at each round

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.593595Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e5355162-5342-46ad-a219-97814f11a395 · outbound

This paper cites The reference point r in calculating hypervol- ume is set to (1, 1).

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning The reference point r in calculating hypervol- ume is set to (1, 1)

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.577932Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0800ac30-8d05-476c-b4ca-501217603c36 · outbound

This paper cites Uci ma- chine learning repository,.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Uci ma- chine learning repository,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.898277Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation eb1791eb-6761-4dc5-9f9e-a870612dce60 · outbound

This paper cites Leveraging asynchronous federated learning to predict customers financial distress.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Leveraging asynchronous federated learning to predict customers financial distress

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.833535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fc901461-8078-494e-bffc-cc6675251b4a · outbound

This paper cites Pareto set learning for neural multi-objective combinato- rial optimization.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Pareto set learning for neural multi-objective combinato- rial optimization

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.801666Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation b5cf120a-0571-4bef-8613-5ddc705ef5e3 · outbound

This paper cites Collaborative fairness in federated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Collaborative fairness in federated learning

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.786161Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation bf62b874-a92e-4ffc-955d-659ef6cc6b56 · outbound

This paper cites Achieving Fairness Across Local and Global Models in Federated Learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Achieving Fairness Across Local and Global Models in Federated Learning

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.335827Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 93e612ff-bd5d-4c34-8a69-dafddddc72cb · outbound

This paper cites Nonlinear multiobjective optimization, volume.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Nonlinear multiobjective optimization, volume

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.761049Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9505791c-d69d-4a9a-bcb6-6199faf86881 · outbound

This paper cites The future of digital health with feder- ated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning The future of digital health with feder- ated learning

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.719010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 61907ea0-c890-4480-8a27-bd4163a22c3d · outbound

This paper cites FairBatch: Batch Selection for Model Fairness.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning FairBatch: Batch Selection for Model Fairness

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.364860Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b53a3f55-afa2-4142-8084-a0ccdd552874 · outbound

This paper cites Fair-fate: Fair federated learning with momentum.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Fair-fate: Fair federated learning with momentum

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.702942Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 5208cc18-1284-45d7-9146-4c08a8bf444a · outbound

This paper cites Evolution Strategies as a Scalable Alternative to Reinforcement Learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Evolution Strategies as a Scalable Alternative to Reinforcement Learning

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.374376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aad4c94c-39e3-478a-9523-fe4ba699e088 · outbound

This paper cites Splitfed: When federated learning meets split learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Splitfed: When federated learning meets split learning

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.687247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 1fbaa6b7-05a2-427e-8df1-31b52d4b773c · outbound

This paper cites Federated Learning with Fair Averaging.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Federated Learning with Fair Averaging

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.383921Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e82f419b-f19b-45c3-b8da-d9ae74cf1439 · outbound

This paper cites Praffl: A preference-aware scheme in fair federated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Praffl: A preference-aware scheme in fair federated learning

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.672324Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 13a04e29-cee8-4ec7-976c-affa9de28cd2 · outbound

This paper cites Improving Fairness via Federated Learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Improving Fairness via Federated Learning

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.398261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fda5cae3-2b50-4da4-852b-5a64f01d2b1a · outbound

This paper cites Federated learning with local fairness con- straints.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Federated learning with local fairness con- straints

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.641707Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 8c89f131-40e4-48cc-a83a-86d0d6ba8a8b · outbound

This paper cites Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.625581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 318e2bab-5ec1-4a98-8dd2-e441465449e0 · outbound

This paper cites 6 shows the concept of Pareto front, Pareto optimal solution and weakly Pareto optimal solution in the problem of minimizing two objectives.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning 6 shows the concept of Pareto front, Pareto optimal solution and weakly Pareto optimal solution in the problem of minimizing two objectives

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.610257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 9874489b-dae0-4a36-be54-38ff501d760a · outbound

This paper cites A data-driven approach to predict the success of bank telemarketing.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning A data-driven approach to predict the success of bank telemarketing

Reference 1999

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.745438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation cb91acc1-d9d4-434c-8088-589f2a5b5125 · outbound

This paper cites Fair Resource Allocation in Federated Learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Fair Resource Allocation in Federated Learning

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.321214Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation af6d886c-1049-4b93-8b8c-02359049f37e · outbound

This paper cites Federated learning for internet of things: A comprehensive survey.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Federated learning for internet of things: A comprehensive survey

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.355478Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc4d7ffb-e1f8-40a7-9745-0a9c91a220dd · outbound

This paper cites A two-timescale stochastic al- gorithm framework for bilevel optimization: Complexity analysis and application to actor-critic.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning A two-timescale stochastic al- gorithm framework for bilevel optimization: Complexity analysis and application to actor-critic

Reference 2015

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.849110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 23dd179f-9294-47a9-a06e-e19a36fee1df · outbound

This paper cites Fairfed: Enabling group fairness in federated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Fairfed: Enabling group fairness in federated learning

Reference 2017

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.883482Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3ac68d99-5154-40fc-8ad1-cafd8c02cb42 · outbound

This paper cites Exploiting shared rep- resentations for personalized federated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Exploiting shared rep- resentations for personalized federated learning

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.943438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 0c0e2519-4156-4d98-856e-1b02ee002659 · outbound

This paper cites Fairness-aware agnostic federated learn- ing.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Fairness-aware agnostic federated learn- ing

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.913447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e69f028d-d00c-4650-88cb-6120a3861fae · outbound

This paper cites Distributionally robust fed- erated averaging.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Distributionally robust fed- erated averaging

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.928461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 871962cd-291a-46e7-8aaf-f130063fbebd · outbound

This paper cites Fairness-aware classifier with prejudice remover regularizer.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Fairness-aware classifier with prejudice remover regularizer

Reference 2022

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.817835Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f193141c-6883-4137-aded-2a0d0584b6c0 · outbound

This paper cites Certifying and removing disparate im- pact.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Certifying and removing disparate im- pact

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.867535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 231d64ec-dec2-465e-8798-64cee661f113 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Communication-efficient learning of deep networks from decentralized data

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-16T05:01:34.341554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ab012040-6e57-4cbb-9898-8d59523733b1 · outbound

This paper cites Gifair-fl: A framework for group and individ- ual fairness in federated learning.

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning Gifair-fl: A framework for group and individ- ual fairness in federated learning

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:01:34.657761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Pith citing papers

No inbound Pith citation observations are available.