Private and Provably Efficient Federated Decision-Making
In this thesis, we study sequential multi-armed bandit and reinforcement learning in the federated setting, where a group of agents collaborates to improve their collective reward by communicating over a network. We first study the multi-armed bandit problem in a decentralized environment. We stu...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2022
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| Online Access: | https://hdl.handle.net/1721.1/143222 |