The growing concerns about data privacy in society lead to restrictions on the computer vision research gradually. Several collaboration-based vision learning methods have recently emerged, e.g., federated learning and split learning. These methods protect user data from leaving local devices, and make training performed only by uploading gradients, parameters, or activations, etc. However, there is little research on collaborative learning based on state-of-the-art and large-scaled models, mainly due to the high computation or communication overheads of the latest models. Training these models may be still unrealized for users’ terminals. In this paper, we make a first attempt at the sensitive image pre-training with large-scaled models in the collaborative learning scenario, and propose a new lightweight framework for split learning based on mask, Masked Split Learning (MaskSL). We further ensure its security by differential privacy. Besides, we model the computation and communication overheads of several collaborative learning approaches by deduction to illustrate advantages of our scheme. Finally, we design and conduct a series of experiments on real-world datasets, e.g., in face recognition and medical image classification tasks, to demonstrate the performance of MaskSL.

中文翻译：

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用于大规模视觉预训练的隐私保护分割学习


社会对数据隐私的日益关注导致计算机视觉研究逐渐受到限制。最近出现了几种基于协作的视觉学习方法，例如联邦学习和分裂学习。这些方法保护用户数据不离开本地设备，并且仅通过上传梯度、参数或激活等来进行训练。然而，基于最先进的大规模模型的协作学习的研究很少。主要是由于最新模型的高计算或通信开销。对于用户终端来说，训练这些模型可能还没有实现。在本文中，我们首次尝试协作学习场景下大规模模型的敏感图像预训练，并提出了一种新的基于掩模的轻量级分割学习框架——Masked Split Learning (MaskSL)。我们通过差分隐私进一步保证其安全性。此外，我们通过演绎对几种协作学习方法的计算和通信开销进行建模，以说明我们方案的优点。最后，我们在现实数据集上设计并进行了一系列实验，例如在人脸识别和医学图像分类任务中，以展示 MaskSL 的性能。