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Communication Dans Un Congrès Année : 2022

SAMBA: A System for Secure Federated Multi-Armed Bandits

Résumé

The federated learning paradigm allows several data owners to contribute to a machine learning task without exposing their potentially sensitive data. We focus on cumulative reward maximization in Multi-Armed Bandits (MAB), a classical reinforcement learning model for decision making under uncertainty. We demonstrate Samba, a generic framework for Secure federAted Multi-armed BAndits. The demonstration platform is a Web interface that simulates the distributed components of Samba, and which helps the data scientist to configure the end-to-end workflow of deploying a federated MAB algorithm. The user-friendly interface of Samba, allows the users to examine the interaction between three key dimensions of federated MAB: cumulative reward, computation time, and security guarantees. We demonstrate Samba with two real-world datasets: Google Local Reviews and Steam Video Game.
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Dates et versions

hal-03754364 , version 1 (19-08-2022)

Identifiants

Citer

Gael Marcadet, Radu Ciucanu, Pascal Lafourcade, Marta Soare, Sihem Amer-Yahia. SAMBA: A System for Secure Federated Multi-Armed Bandits. 2022 IEEE 38th International Conference on Data Engineering (ICDE), May 2022, Online (hosted in Kuala Lumpur), Malaysia. pp.3154-3157, ⟨10.1109/ICDE53745.2022.00286⟩. ⟨hal-03754364⟩
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