267 research outputs found
Federated Linear Contextual Bandits with User-level Differential Privacy
This paper studies federated linear contextual bandits under the notion of
user-level differential privacy (DP). We first introduce a unified federated
bandits framework that can accommodate various definitions of DP in the
sequential decision-making setting. We then formally introduce user-level
central DP (CDP) and local DP (LDP) in the federated bandits framework, and
investigate the fundamental trade-offs between the learning regrets and the
corresponding DP guarantees in a federated linear contextual bandits model. For
CDP, we propose a federated algorithm termed as \robin and show that it is
near-optimal in terms of the number of clients and the privacy budget
by deriving nearly-matching upper and lower regret bounds when
user-level DP is satisfied. For LDP, we obtain several lower bounds, indicating
that learning under user-level -LDP must suffer a regret
blow-up factor at least { or
} under different conditions.Comment: Accepted by ICML 202
Decentralized Exploration in Multi-Armed Bandits
We consider the decentralized exploration problem: a set of players
collaborate to identify the best arm by asynchronously interacting with the
same stochastic environment. The objective is to insure privacy in the best arm
identification problem between asynchronous, collaborative, and thrifty
players. In the context of a digital service, we advocate that this
decentralized approach allows a good balance between the interests of users and
those of service providers: the providers optimize their services, while
protecting the privacy of the users and saving resources. We define the privacy
level as the amount of information an adversary could infer by intercepting the
messages concerning a single user. We provide a generic algorithm Decentralized
Elimination, which uses any best arm identification algorithm as a subroutine.
We prove that this algorithm insures privacy, with a low communication cost,
and that in comparison to the lower bound of the best arm identification
problem, its sample complexity suffers from a penalty depending on the inverse
of the probability of the most frequent players. Then, thanks to the genericity
of the approach, we extend the proposed algorithm to the non-stationary
bandits. Finally, experiments illustrate and complete the analysis
Corrupt Bandits for Preserving Local Privacy
We study a variant of the stochastic multi-armed bandit (MAB) problem in
which the rewards are corrupted. In this framework, motivated by privacy
preservation in online recommender systems, the goal is to maximize the sum of
the (unobserved) rewards, based on the observation of transformation of these
rewards through a stochastic corruption process with known parameters. We
provide a lower bound on the expected regret of any bandit algorithm in this
corrupted setting. We devise a frequentist algorithm, KLUCB-CF, and a Bayesian
algorithm, TS-CF and give upper bounds on their regret. We also provide the
appropriate corruption parameters to guarantee a desired level of local privacy
and analyze how this impacts the regret. Finally, we present some experimental
results that confirm our analysis
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