Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing
Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing
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DOI：
10.1016/j.eswa.2023.122847
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发表时间：
2023-04
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影响因子：
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通讯作者：
An-dong Li;Milan Markovic;P. Edwards;G. Leontidis
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作者：
An-dong Li;Milan Markovic;P. Edwards;G. Leontidis
联邦学习 (FL) 提出了一种去中心化的农业食品领域模型训练方法，并提供了提高机器学习性能的潜力，同时确保各个农场或数据孤岛的安全和隐私。然而，传统的 FL 方法有两个主要局限性。首先，各个孤岛上的异构数据可能会导致全局模型对某些客户端（但不是所有客户端）表现良好，因为某些客户端的更新方向可能会在聚合后阻碍其他客户端。其次，它缺乏关于 FL 和大模型尺寸期间的通信成本的效率观点。本文提出了一种新技术解决方案，在客户端模型上利用网络剪枝并聚合剪枝后的模型。该方法使本地模型能够根据各自的数据分布进行定制，并减轻农业食品数据中存在的数据异质性。此外，它允许更紧凑的模型，在传输过程中消耗更少的数据。我们用大豆产量预测数据集进行实验，发现与 FedAvg 相比，这种方法可以将推理性能提高 15.5% 至 20%，同时将本地模型大小减少多达 84%，并将客户端和服务器之间通信的数据量减少 57.1% 至 64.7%。我们的方法展示了使用更环保的高效模型来支持农业食品行业向净零转型的潜力。该方法的未来改进可以进一步优化农业食品的分布式学习，增强可持续性和适用性。
Federated Learning (FL) presents a decentralized approach to model training in the agri-food sector and offers the potential for improved machine learning performance, while ensuring the safety and privacy of individual farms or data silos. However, the conventional FL approach has two major limitations. First, the heterogeneous data on individual silos can cause the global model to perform well for some clients but not all, as the update direction on some clients may hinder others after they are aggregated. Second, it is lacking with respect to the efficiency perspective concerning communication costs during FL and large model sizes. This paper proposes a new technical solution that utilizes network pruning on client models and aggregates the pruned models. This method enables local models to be tailored to their respective data distribution and mitigate the data heterogeneity present in agri-food data. Moreover, it allows for more compact models that consume less data during transmission. We experiment with a soybean yield forecasting dataset and find that this approach can improve inference performance by 15.5% to 20% compared to FedAvg, while reducing local model sizes by up to 84% and the data volume communicated between the clients and the server by 57.1% to 64.7%. Our method demonstrates the potential to use efficient models that are more environmentally friendly to support the agri-food sector’s transition to net zero. Future enhancements of this method could further optimize distributed learning in agri-food, enhancing sustainability and applicability.