Multi-Party Computation (MPC) provides an effective cryptographic solution for distributed computing systems so that local models with sensitive information are encrypted before sending to the centralized servers for aggregation. Though direct local knowledge leakages are eliminated in MPC-based algorithms, we observe the server can still obtain the local information indirectly in many scenarios, or even reveal the groundtruth images through methods like Deep Leakage from Gradients (DLG). To eliminate such possibilities and provide stronger protections, we propose an augmented MPC approach by encrypting local models with two rounds of decomposition before transmitting to the server. The proposed solution allows us to remove the constraint that servers must be honest in the general federated learning settings since the true global model is hidden from the servers. Specifically, the augmented MPC algorithm encodes local models into multiple secret shares in the first round, then each share is furthermore split into a public share and a private share. Consequences of such a two-round decomposition are that the augmented algorithm fully inherits the advantages of standard MPC by providing lossless encryption and decryption while simultaneously rendering the global model invisible to the central server. Both theoretical analysis and experimental verification demonstrate that such an augmented solution can provide stronger protections for the security and privacy of the training data, with minimal extra communication and computation costs incurred.

The goal of online active learning is to learn predictive models from a sequence of unlabeled data given limited label query budget. Unlike conventional online learning tasks, online active learning is considerably more challenging since the online learner not only needs to decide how to update the predictive models effectively whenever a labeled training sample is available, but also needs to determine when is appropriate to query the label of an incoming unlabeled instance given limited label query budget. Most existing approaches for online active learning are often based on the family of first-order online learning algorithms, which are simple and efficient but falls short in slow convergence and sub-optimal solution in exploiting the labeled training data. To conquer these limitations, this paper presents a novel framework of Second-order Online Active Learning (SOAL). To further make SOAL practical for real-world applications, especially for class-imbalanced online classification tasks (e.g., malicious web detection), we extend the SOAL framework by proposing the Cost-sensitive Second-order Online Active Learning algorithm named “SOAL$_{CS}$”. We conducted both theoretical analysis and empirical studies, in which the promising empirical results validate the efficacy and scalability of the proposed online active learning algorithms towards large-scale online learning tasks.

In this article, we propose a method, generative image reconstruction from gradients (GIRG), for recovering training images from gradients in a federated learning (FL) setting, where privacy is preserved by sharing model weights and gradients rather than raw training data. Previous studies have shown the potential for revealing clients’ private information or even pixel-level recovery of training images from shared gradients. However, existing methods are limited to low-resolution images and small batch sizes (BSs) or require prior knowledge about the client data. GIRG utilizes a conditional generative model to reconstruct training images and their corresponding labels from the shared gradients. Unlike previous generative model-based methods, GIRG does not require prior knowledge of the training data. Furthermore, GIRG optimizes the weights of the conditional generative model to generate highly accurate “dummy” images instead of optimizing the input vectors of the generative model. Comprehensive empirical results show that GIRG is able to recover high-resolution images with large BSs and can even recover images from the aggregation of gradients from multiple participants. These results reveal the vulnerability of current FL practices and call for immediate efforts to prevent inversion attacks in gradient-sharing-based collaborative training.