Self-Supervised On-Device Federated Learning From Unlabeled Streams
Self-Supervised On-Device Federated Learning From Unlabeled Streams
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DOI：
10.1109/tcad.2023.3274956
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发表时间：
2022-12
影响因子：
2.9
通讯作者：
Jiahe Shi;Yawen Wu;Dewen Zeng;Jun Tao;Jingtong Hu;Yiyu Shi
中科院分区：
文献类型：
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作者：
Jiahe Shi;Yawen Wu;Dewen Zeng;Jun Tao;Jingtong Hu;Yiyu Shi
边缘设备的无处不在导致边缘产生的无标注数据量不断增加。部署在边缘设备上的深度学习模型需要从这些无标注数据中学习，以不断提高准确性。自监督表征学习利用集中式无标注数据取得了有前景的性能。然而，对隐私保护的日益关注限制了将边缘设备上的分布式无标注图像数据集中化。虽然联邦学习已被广泛采用以实现具有隐私保护的分布式机器学习，但由于没有一种数据选择方法来高效地选择流数据，传统的联邦学习框架无法在边缘有限的存储资源下处理这些大量的分散式无标注数据。为了应对这些挑战，我们提出了一种具有核心集选择的自监督设备上联邦学习框架，我们称之为SOFed，它能在每个设备上自动选择一个由最具代表性的样本组成的核心集放入重放缓冲区。它保护了数据隐私，因为每个客户端在学习良好的视觉表征时不共享原始数据。实验证明了所提方法在视觉表征学习中的有效性和重要性。
The ubiquity of edge devices has led to a growing amount of unlabeled data produced at the edge. Deep learning models deployed on edge devices are required to learn from these unlabeled data to continuously improve accuracy. Self-supervised representation learning has achieved promising performances using centralized unlabeled data. However, the increasing awareness of privacy protection limits centralizing the distributed unlabeled image data on edge devices. While federated learning has been widely adopted to enable distributed machine learning with privacy preservation, without a data selection method to efficiently select streaming data, the traditional federated learning framework fails to handle these huge amounts of decentralized unlabeled data with limited storage resources on edge. To address these challenges, we propose a self-supervised on-device federated learning framework with coreset selection, which we call SOFed, to automatically select a coreset that consists of the most representative samples into the replay buffer on each device. It preserves data privacy as each client does not share raw data while learning good visual representations. Experiments demonstrate the effectiveness and significance of the proposed method in visual representation learning.