Biblio

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired.In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.

General sparse matrix-matrix multiplication (SpGEMM) is a core component of many algorithms. A number of recent works have used high throughput graphics processing units (GPUs) to accelerate SpGEMM. However, exploiting the power of GPUs for SpGEMM requires addressing a number of challenges, including highly imbalanced workloads and large numbers of inefficient random global memory accesses. This paper presents a SpGEMM algorithm which uses several novel techniques to overcome these problems. We first propose two low cost methods to achieve perfect load balancing during the most expensive step in SpGEMM. Next, we show how to eliminate nearly all random global memory accesses using shared memory based hash tables. To optimize the performance of the hash tables, we propose a lightweight method to estimate the number of nonzeros in the output matrix. We compared our algorithm to the CUSP, CUSPARSE and the state-of-the-art BHSPARSE GPU SpGEMM algorithms, and show that it performs 5.6x, 2.4x and 1.5x better on average, and up to 11.8x, 9.5x and 2.5x better in the best case, respectively. Furthermore, we show that our algorithm performs especially well on highly imbalanced and unstructured matrices.