Federated learning reduces the risk of information leakage, but remains vulnerable to attack. We show that well-mixed gradients provide numerical resistance to gradient inversion in neural networks. For example, we can enhance mixing gradients in a batch by choosing an appropriate loss function and drawing identical labels, and we support this with an approximate solution of batch inversion for linear layers. These simple architecture choices show no degradation to classification performance as opposed to noise perturbation defense. To accurately assess data recovery, we propose to use a variation distance metric for information leakage in images, derived from total variation. In contrast to Mean Squared Error or Structural Similarity Index metrics, it provides a continuous metric for information recovery. Finally, our empirical results of information recovery from various inversion attacks and training performance supports our defense strategies. These simple architecture choices found to be also useful for practical size of convolutional neural networks but depends on their size. We hope this work will trigger further defense studies using gradient mixing, towards achieving a trustful federation policy.

Machine learning on source code (MLOnCode) is a popular research field that has been driven by the availability of large-scale code repositories and the development of powerful probabilistic and deep learning models for mining source code. Code-to-code recommendation is a task in MLOnCode that aims to recommend relevant, diverse and concise code snippets that usefully extend the code currently being written by a developer in their development environment (IDE). Code-to-code recommendation engines hold the promise of increasing developer productivity by reducing context switching from the IDE and increasing code-reuse. Existing code-to-code recommendation engines do not scale gracefully to large codebases, exhibiting a linear growth in query time as the code repository increases in size. In addition, existing code-to-code recommendation engines fail to account for the global statistics of code repositories in the ranking function, such as the distribution of code snippet lengths, leading to sub-optimal retrieval results. We address both of these weaknesses with Senatus, a new code-to-code recommendation engine. At the core of Senatus is De-Skew LSH a new locality sensitive hashing (LSH) algorithm that indexes the data for fast (sub-linear time) retrieval while also counteracting the skewness in the snippet length distribution using novel abstract syntax tree-based feature scoring and selection algorithms. We evaluate Senatus and find the recommendations to be of higher quality than competing baselines, while achieving faster search. For example on the CodeSearchNet dataset Senatus improves performance by 31.21% F1 and 147.9x faster query time compared to Facebook Aroma. Senatus also outperforms standard MinHash LSH by 29.2% F1 and 51.02x faster query time.