Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus.

Lin M, Yuan G, Liu D.

DOI: 10.1109/tcyb.2025.3606528
Published: 2026-02-01
Container: IEEE Trans Cybern
Publisher: Not recorded
Open access: no

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Cite this work

BibTeX

@article{allodium:10.1109/tcyb.2025.3606528,
  title = {Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus.},
  author = {Lin M and  Yuan G and  Liu D.},
  year = {2026},
  journal = {IEEE Trans Cybern},
  doi = {10.1109/tcyb.2025.3606528},
  url = {https://doi.org/10.1109/tcyb.2025.3606528}
}

RIS

TY  - JOUR
TI  - Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus.
AU  - Lin M
AU  -  Yuan G
AU  -  Liu D.
PY  - 2026
JO  - IEEE Trans Cybern
DO  - 10.1109/tcyb.2025.3606528
UR  - https://doi.org/10.1109/tcyb.2025.3606528
ER  -

APA

M, L., G, Y., & D., L. (2026). Federated Learning Adaptive Dynamic Programming for Massive Multiagent Mean-Field Games-Based Optimal Consensus.. IEEE Trans Cybern. https://doi.org/10.1109/tcyb.2025.3606528

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europe-pmc · retrieved 2026-09-25T12:28:32.296Z