Federated Multi-armed Bandits with Personalization
arXiv:2102.13101
Abstract
A general framework of personalized federated multi-armed bandits (PF-MAB) is proposed, which is a new bandit paradigm analogous to the federated learning (FL) framework in supervised learning and enjoys the features of FL with personalization. Under the PF-MAB framework, a mixed bandit learning problem that flexibly balances generalization and personalization is studied. A lower bound analysis for the mixed model is presented. We then propose the Personalized Federated Upper Confidence Bound (PF-UCB) algorithm, where the exploration length is chosen carefully to achieve the desired balance of learning the local model and supplying global information for the mixed learning objective. Theoretical analysis proves that PF-UCB achieves an regret regardless of the degree of personalization, and has a similar instance dependency as the lower bound. Experiments using both synthetic and real-world datasets corroborate the theoretical analysis and demonstrate the effectiveness of the proposed algorithm.
Accepted to AISTATS 2021, oral presentation
References in corpus (11)
- Communication-Efficient Learning of Deep Networks from Decentralized Data
- A Contextual-Bandit Approach to Personalized News Article Recommendation
- Distributed Learning in Multi-Armed Bandit with Multiple Players
- Improving Federated Learning Personalization via Model Agnostic Meta Learning
- Personalized Federated Learning: A Meta-Learning Approach
- Adaptive Personalized Federated Learning
- Three Approaches for Personalization with Applications to Federated Learning
- Federated Learning of a Mixture of Global and Local Models
- Federated Bandit: A Gossiping Approach
- Federated Residual Learning
- Multiplayer Bandit Learning, from Competition to Cooperation