Exploiting Heterogeneity in Robust Federated Best-Arm Identification
- URL: http://arxiv.org/abs/2109.05700v2
- Date: Wed, 15 Sep 2021 15:24:33 GMT
- Title: Exploiting Heterogeneity in Robust Federated Best-Arm Identification
- Authors: Aritra Mitra, Hamed Hassani and George Pappas
- Abstract summary: Fed-SEL is a simple communication-efficient algorithm that builds on successive elimination techniques and involves local sampling steps at the clients.
We show that for certain heterogeneous problem instances, Fed-SEL outputs the best-arm after just one round of communication.
As our final contribution, we develop variants of Fed-SEL, both for federated and peer-to-peer settings, that are robust to the presence of Byzantine clients.
- Score: 19.777265059976337
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We study a federated variant of the best-arm identification problem in
stochastic multi-armed bandits: a set of clients, each of whom can sample only
a subset of the arms, collaborate via a server to identify the best arm (i.e.,
the arm with the highest mean reward) with prescribed confidence. For this
problem, we propose Fed-SEL, a simple communication-efficient algorithm that
builds on successive elimination techniques and involves local sampling steps
at the clients. To study the performance of Fed-SEL, we introduce a notion of
arm-heterogeneity that captures the level of dissimilarity between
distributions of arms corresponding to different clients. Interestingly, our
analysis reveals the benefits of arm-heterogeneity in reducing both the sample-
and communication-complexity of Fed-SEL. As a special case of our analysis, we
show that for certain heterogeneous problem instances, Fed-SEL outputs the
best-arm after just one round of communication. Our findings have the following
key implication: unlike federated supervised learning where recent work has
shown that statistical heterogeneity can lead to poor performance, one can
provably reap the benefits of both local computation and heterogeneity for
federated best-arm identification. As our final contribution, we develop
variants of Fed-SEL, both for federated and peer-to-peer settings, that are
robust to the presence of Byzantine clients, and hence suitable for deployment
in harsh, adversarial environments.
Related papers
- Best Arm Identification with Minimal Regret [55.831935724659175]
Best arm identification problem elegantly amalgamates regret minimization and BAI.
Agent's goal is to identify the best arm with a prescribed confidence level.
Double KL-UCB algorithm achieves optimality as the confidence level tends to zero.
arXiv Detail & Related papers (2024-09-27T16:46:02Z) - Representative Arm Identification: A fixed confidence approach to identify cluster representatives [7.459521930846415]
We study the representative arm identification problem in the multi-armed bandits (MAB) framework.
The RAI problem covers as special cases several well-studied MAB problems such as identifying the best arm or any $M$ out of the top $K$.
We propose two algorithms, based on the idea of confidence intervals, and provide high probability upper bounds on their sample complexity.
arXiv Detail & Related papers (2024-08-26T11:47:52Z) - Best Arm Identification with Fixed Budget: A Large Deviation Perspective [54.305323903582845]
We present sred, a truly adaptive algorithm that can reject arms in it any round based on the observed empirical gaps between the rewards of various arms.
In particular, we present sred, a truly adaptive algorithm that can reject arms in it any round based on the observed empirical gaps between the rewards of various arms.
arXiv Detail & Related papers (2023-12-19T13:17:43Z) - Pure Exploration under Mediators' Feedback [63.56002444692792]
Multi-armed bandits are a sequential-decision-making framework, where, at each interaction step, the learner selects an arm and observes a reward.
We consider the scenario in which the learner has access to a set of mediators, each of which selects the arms on the agent's behalf according to a and possibly unknown policy.
We propose a sequential decision-making strategy for discovering the best arm under the assumption that the mediators' policies are known to the learner.
arXiv Detail & Related papers (2023-08-29T18:18:21Z) - Covariance Adaptive Best Arm Identification [0.0]
The goal is to identify the arm with the highest mean reward with a probability of at least 1 -- $delta$, while minimizing the number of arm pulls.
We propose a more flexible scenario where arms can be dependent and rewards can be sampled simultaneously.
This framework is relevant in various applications, such as clinical trials, where similarities between patients or drugs suggest underlying correlations.
arXiv Detail & Related papers (2023-06-05T06:57:09Z) - On the Complexity of Multi-Agent Decision Making: From Learning in Games
to Partial Monitoring [105.13668993076801]
A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees.
We study this question in a general framework for interactive decision making with multiple agents.
We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
arXiv Detail & Related papers (2023-05-01T06:46:22Z) - Combating Exacerbated Heterogeneity for Robust Models in Federated
Learning [91.88122934924435]
Combination of adversarial training and federated learning can lead to the undesired robustness deterioration.
We propose a novel framework called Slack Federated Adversarial Training (SFAT)
We verify the rationality and effectiveness of SFAT on various benchmarked and real-world datasets.
arXiv Detail & Related papers (2023-03-01T06:16:15Z) - FedSkip: Combatting Statistical Heterogeneity with Federated Skip
Aggregation [95.85026305874824]
We introduce a data-driven approach called FedSkip to improve the client optima by periodically skipping federated averaging and scattering local models to the cross devices.
We conduct extensive experiments on a range of datasets to demonstrate that FedSkip achieves much higher accuracy, better aggregation efficiency and competing communication efficiency.
arXiv Detail & Related papers (2022-12-14T13:57:01Z) - Federated Multi-Armed Bandits Under Byzantine Attacks [8.974667651758095]
Federated multi-armed bandits (FMAB) is an emerging framework where learners play an MAB game and communicate their aggregated feedback to a server to learn a globally optimal arm.
We study the FMAB problem in the presence of Byzantine clients who can send false model updates threatening the learning process.
We propose a median-of-means (MoM)-based online algorithm, Fed-MoM-UCB, to cope with Byzantine clients.
arXiv Detail & Related papers (2022-05-09T09:06:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.