Paper Citation Record · LEDGER

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters

As of 13 August 2026, this Paper Citation Record lists 28 of 28 outbound references and 0 inbound Pith citation observations for arXiv:2506.15825.

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

pith.paper-citation-record.v1
2506.15825 v1

Coverage vector

measured 28 of 28 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T23:56:32.140259Z

measured 28 of 28 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+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

28 of 28 outbound references displayed

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verified fuzzy17
unresolved11
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 7164331d-431e-409e-8520-39928b46ae5f · outbound

This paper cites Deep reinforcement learning from human preferences.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Deep reinforcement learning from human preferences

Reference 1

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Observation 2d97fcd8-91bd-49a6-8f44-7947c1d67604 · outbound

This paper cites Reinforcement learning from simulated environments: An encoder decoder framework.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Reinforcement learning from simulated environments: An encoder decoder framework

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:35.213085Z

Source-reported events for the cited work

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Observation 917dc54c-3aa9-43b0-819c-943a25dd2e94 · outbound

This paper cites Federated Reinforcement Learning: Techniques, Applications, and Open Challenges.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated Reinforcement Learning: Techniques, Applications, and Open Challenges

Reference 3

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Observation 354695c2-933d-484e-92c0-9572eee6c357 · outbound

This paper cites Release Strategies and the Social Impacts of Language Models.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Release Strategies and the Social Impacts of Language Models

Reference 4

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Source-reported events for the cited work

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Observation ae6d449b-3ec7-4e61-8440-5e69c1786955 · outbound

This paper cites Mastering the game of go with deep neural networks and tree search.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Mastering the game of go with deep neural networks and tree search

Reference 5

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Observation 7262c23a-1095-4d0b-b055-7827e44744d0 · outbound

This paper cites Multi-task learning with attention for end-to-end autonomous driving.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Multi-task learning with attention for end-to-end autonomous driving

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:35.051741Z

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Observation 620821e2-97d9-4989-a3b0-67476ea12767 · outbound

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

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Communication- efficient learning of deep networks from decentralized data

Reference 7

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Source-reported events for the cited work

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Observation d2d38a7c-3349-4f07-9387-56b197d7adc8 · outbound

This paper cites Model fusion via optimal transport.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Model fusion via optimal transport

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T23:56:29.879214Z

Source-reported events for the cited work

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Observation d9a3416a-9131-4dbf-98ad-2b60279ea504 · outbound

This paper cites Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Wasserstein Barycenter-based Model Fusion and Linear Mode Connectivity of Neural Networks

Reference 9

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Observation 4ebc682b-3297-4438-b3c0-0243ea89da39 · outbound

This paper cites Federated machine learning: Concept and applications.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated machine learning: Concept and applications

Reference 10

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Source-reported events for the cited work

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Observation d7528d91-52ee-42b8-95c7-c6b3efb8f884 · outbound

This paper cites Federated learning with differential privacy: Algorithms and performance analysis.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated learning with differential privacy: Algorithms and performance analysis

Reference 11

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Source-reported events for the cited work

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Observation 7dc71a71-8fe5-48e1-8284-1517eb8a7715 · outbound

This paper cites Federated reinforcement learning with environment heterogeneity.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated reinforcement learning with environment heterogeneity

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:34.725019Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 7be65102-f0e8-4408-bfa3-9185c3c77449 · outbound

This paper cites Federated reinforcement learning: Linear speedup under markovian sampling.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated reinforcement learning: Linear speedup under markovian sampling

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:34.592201Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e467eb8b-2cf3-4b2b-9d44-b2a524d3a670 · outbound

This paper cites Federated reinforcement learning acceleration method for precise control of multiple devices.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated reinforcement learning acceleration method for precise control of multiple devices

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:34.429374Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 196df1ef-8f5d-4070-9575-e4e250390699 · outbound

This paper cites Semi-distributed resource management in uav-aided mec systems: A multi-agent federated reinforcement learning approach.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Semi-distributed resource management in uav-aided mec systems: A multi-agent federated reinforcement learning approach

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:34.286620Z

Source-reported events for the cited work

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Observation 3c0e5dee-b9aa-4b9b-92e4-7067c314e90d · outbound

This paper cites Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Federated reinforcement learning for energy management of multiple smart homes with distributed energy resources

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:34.127440Z

Source-reported events for the cited work

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Observation 9c26fc6e-9919-4bfc-9b1e-b473f55e6c83 · outbound

This paper cites Deep learning.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Deep learning

Reference 17

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Observation e37f87e9-1948-46a7-a173-918a838d6dfc · outbound

This paper cites Mini-batch gradient descent: Faster con- vergence under data sparsity.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Mini-batch gradient descent: Faster con- vergence under data sparsity

Reference 18

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Source-reported events for the cited work

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Observation c7b4b4d9-bc16-4390-9490-9818f0ab241e · outbound

This paper cites Introduction to multi-layer feed-forward neural networks.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Introduction to multi-layer feed-forward neural networks

Reference 19

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Source-reported events for the cited work

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Observation eb232aac-8534-4fb0-b183-9399731ca79c · outbound

This paper cites Mémoire sur la théorie des déblais et des remblais.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Mémoire sur la théorie des déblais et des remblais

Reference 20

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Observation e5f1f19b-7ffc-428f-824a-17a0653afaa3 · outbound

This paper cites On the transfer of masses: Doklady akademii nauk ussr.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters On the transfer of masses: Doklady akademii nauk ussr

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:33.646414Z

Source-reported events for the cited work

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Observation 93de12d4-91ca-418c-9779-d1319ab02f07 · outbound

This paper cites Computational optimal transport: With applications to data science.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Computational optimal transport: With applications to data science

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:33.448793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 0fe0c74b-6d9d-482b-86d9-0feebe3c869a · outbound

This paper cites Genesis of bimodal distributions.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Genesis of bimodal distributions

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:33.268998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1870b0ca-1856-4962-89f3-b9695eaf7fa9 · outbound

This paper cites Reinforcement learning: A survey.Journal of Machine Learning Research, 18(153):1–90, 2017.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Reinforcement learning: A survey.Journal of Machine Learning Research, 18(153):1–90, 2017

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:33.081894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a0bbf6d7-f5a5-423d-b435-fc495aaf4ac3 · outbound

This paper cites Optimal control theory: an introduction.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Optimal control theory: an introduction

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:32.855118Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation a1ccf592-8d38-4516-b5ad-f043f0842077 · outbound

This paper cites Robust estimation of a location parameter.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Robust estimation of a location parameter

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:32.657143Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d412b02f-6c16-4605-9475-c7e135ddaf0e · outbound

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Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Unresolved cited work

Reference 27

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 12e141f0-20ac-4426-a6c7-f7624f0c2958 · outbound

This paper cites Human-level control through deep reinforcement learning.

Heterogeneous Federated Reinforcement Learning Using Wasserstein Barycenters Human-level control through deep reinforcement learning

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T23:56:32.313606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

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