Communication Efficient Federated Learning for Generalized Linear Bandits-猫眼课题宝

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发表时间：

2022-02

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ArXiv

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其他

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作者：

Chuanhao Li;Hongning Wang

近年来，为了满足数据分散和将学习过程推到客户端的需求，在联邦学习环境下研究了上下文Bandit算法。但受限于所需的通信效率，现有的解决方案被限制为线性模型，以利用其封闭形式的解决方案进行参数估计。这样一个有限的模型选择极大地阻碍了这些算法的实际效用。在本文中，我们通过研究联邦学习设置下的广义线性强盗模型，迈出了解决这一挑战的第一步。我们提出了一个有效的解决方案框架，采用在线回归本地更新和离线回归的全球更新。我们严格证明，虽然设置是更一般和具有挑战性的，我们的算法可以达到次线性率的遗憾和通信成本，这也是我们广泛的实证评估验证。

Contextual bandit algorithms have been recently studied under the federated learning setting to satisfy the demand of keeping data decentralized and pushing the learning of bandit models to the client side. But limited by the required communication efficiency, existing solutions are restricted to linear models to exploit their closed-form solutions for parameter estimation. Such a restricted model choice greatly hampers these algorithms' practical utility. In this paper, we take the first step to addressing this challenge by studying generalized linear bandit models under the federated learning setting. We propose a communication-efficient solution framework that employs online regression for local update and offline regression for global update. We rigorously proved, though the setting is more general and challenging, our algorithm can attain sub-linear rate in both regret and communication cost, which is also validated by our extensive empirical evaluations.

Communication Efficient Federated Learning for Generalized Linear Bandits

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