Few-shot learning aims at training a model that can effectively recognize novel classes with extremely limited training examples. Few-shot learning via meta-learning can improve the performance on novel tasks by leveraging previously acquired knowledge as a prior when the training examples are extremely limited. However, most of these existing few-shot learning methods involve parameter transfer, which usually requires sharing models trained on the examples for specific tasks, thus posing a potential threat to the privacy of data owners. To tackle this, we design a novel secure collaborative few-shot learning framework. More specifically, we incorporate differential privacy into few-shot learning through adding the calibrated Gaussian noise to its optimization process to prevent sensitive information in the training set from being leaked. To prevent potential privacy disclosure to other participants and the central server, homomorphic encryption is integrated while calculating global loss functions and interacting with a central server. Furthermore, we implement our framework on the classical few-shot learning methods such as MAML and Reptile, and extensively evaluate its performance on Omniglot, Mini-ImageNet and Fewshot-CIFAR100 datasets. The experimental results demonstrate the effectiveness of our framework in both utility and privacy.

很少有的学习旨在训练一个模型，该模型可以通过极其有限的训练示例有效地识别新颖的课程。当训练示例极为有限时，通过利用先前获得的知识作为先验，通过元学习进行的少量学习可以提高新任务的性能。但是，大多数这些现有的快速学习方法都涉及参数传递，这通常需要共享在示例中针对特定任务训练的模型，从而对数据所有者的隐私构成潜在威胁。为了解决这个问题，我们设计了一种新颖的安全协作式几次学习框架。更具体地说，我们通过将经过校准的高斯噪声添加到优化过程中，将差异性隐私纳入快速学习中，以防止泄漏训练集中的敏感信息。为了防止潜在的隐私泄露给其他参与者和中央服务器，在计算全局丢失函数并与中央服务器交互时，同态加密被集成。此外，我们在MAML和Reptile等经典的少量快照学习方法上实现了我们的框架，并广泛评估了它在Omniglot，Mini-ImageNet和Fewshot-CIFAR100数据集上的性能。实验结果证明了我们的框架在实用程序和隐私方面的有效性。并广泛评估其在Omniglot，Mini-ImageNet和Fewshot-CIFAR100数据集上的性能。实验结果证明了我们的框架在实用程序和隐私方面的有效性。并广泛评估其在Omniglot，Mini-ImageNet和Fewshot-CIFAR100数据集上的性能。实验结果证明了我们的框架在实用程序和隐私方面的有效性。