AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning
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DOI：
10.1145/3466752.3480129
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发表时间：
2021-07
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影响因子：
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通讯作者：
Young Geun Kim;Carole-Jean Wu
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文献类型：
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作者：
Young Geun Kim;Carole-Jean Wu
联邦学习使一组分散的移动设备能够在边缘协作训练共享的机器学习模型，同时将所有原始训练样本保留在设备上。这种分散的训练方法被证明是一种减轻隐私泄露风险的实用解决方案。然而，由于非iid训练数据分布、广泛的系统异质性和现场随机变化的运行时效应，在边缘实现高效的FL部署是具有挑战性的。本文通过考虑边缘执行的随机性，共同优化了最先进的FL用例的收敛时间和能源效率。我们通过定制设计一种强化学习算法来提出AutoFL，该算法在随机运行时方差、系统和数据异质性存在的情况下，学习并确定每个FL模型聚合轮的哪K个参与设备和每个设备执行目标。通过明智地考虑FL边缘部署的独特特性，AutoFL在K个参与者的集群中，为本地客户和全球客户分别实现了3.6倍的模型收敛时间和4.7倍和5.2倍的能源效率提高。
Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device. This decentralized training approach is demonstrated as a practical solution to mitigate the risk of privacy leakage. However, enabling efficient FL deployment at the edge is challenging because of non-IID training data distribution, wide system heterogeneity and stochastic-varying runtime effects in the field. This paper jointly optimizes time-to-convergence and energy efficiency of state-of-the-art FL use cases by taking into account the stochastic nature of edge execution. We propose AutoFL by tailor-designing a reinforcement learning algorithm that learns and determines which K participant devices and per-device execution targets for each FL model aggregation round in the presence of stochastic runtime variance, system and data heterogeneity. By considering the unique characteristics of FL edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence time and 4.7 and 5.2 times higher energy efficiency for local clients and globally over the cluster of K participants, respectively.