Samba: A System for Secure Federated Multi-Armed Bandits

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 uncertain...

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Bibliographic Details
Published in2022 IEEE 38th International Conference on Data Engineering (ICDE) pp. 3154 - 3157
Main Authors Marcadet, Gael, Ciucanu, Radu, Lafourcade, Pascal, Soare, Marta, Amer-Yahia, Sihem
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2022
Subjects
Conferences
Data engineering
Decision making
Distributed databases
federated learning
Games
machine learning security
multi-armed bandits
Reinforcement learning
secure protocol
Uncertainty
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Summary: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.
ISSN:2375-026X
DOI:10.1109/ICDE53745.2022.00286