FeReD: Federated Reinforcement Learning in the DBMS
In: CIKM '22: The 31st ACM International Conference on Information and Knowledge Management ; https://hal.science/hal-03819735 ; CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, Oct 2022, Atlanta GA, United States. pp.4989-4993, ⟨10.1145/3511808.3557203⟩, 2022
Konferenz
Zugriff:
International audience ; Federated learning enables clients to enrich their locally trained models via updates performed by a coordination server based on aggregates of local models. There are multiple advances in methods and applications of federated learning, in particular in cross-device federation, where clients having limited data and computational resources collaborate in a joint learning problem. Given the constraint of limited resources in cross-device federation, we study the potential benefits of embedded in-DBMS learning, illustrated in a federated reinforcement learning problem. We demonstrate FeReD, a system that contrasts the performance of cross-device federation using Q-learning, a popular reinforcement learning algorithm. FeReD offers step-by-step guidance for in-DBMS SQLite implementation challenges for both horizontal and vertical data partitioning. FeReD also allows to contrast the Q-learning implementations in SQLite vs a standard Python implementation, by highlighting their learning performance, computational efficiency, succinctness and expressiveness. A video of FeReD is available at https://www.youtube.com/watch?v=2kRIu_C5RZA and its open source code at https://github.com/sotostzam/FeReD.
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FeReD: Federated Reinforcement Learning in the DBMS
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Autor/in / Beteiligte Person: | Tzamaras, Sotirios ; Ciucanu, Radu ; Soare, Marta ; Amer-Yahia, Sihem ; Université Grenoble Alpes (UGA) ; Laboratoire d'Informatique de Grenoble (LIG) ; Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP ) ; ANR-19-P3IA-0003,MIAI,MIAI @ Grenoble Alpes(2019) ; European Project: H2020,INODE ; European Project: 952215,TAILOR(2020) |
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Zeitschrift: | CIKM '22: The 31st ACM International Conference on Information and Knowledge Management ; https://hal.science/hal-03819735 ; CIKM '22: The 31st ACM International Conference on Information and Knowledge Management, Oct 2022, Atlanta GA, United States. pp.4989-4993, ⟨10.1145/3511808.3557203⟩, 2022 |
Veröffentlichung: | HAL CCSD ; ACM, 2022 |
Medientyp: | Konferenz |
DOI: | 10.1145/3511808.3557203 |
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