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Found 16 papers, 8 papers with code
Advances and Open Problems in Federated Learning
FL embodies the principles of focused data collection and minimization, and can mitigate many of the systemic privacy risks and costs resulting from traditional, centralized machine learning and data science approaches.
Opacus: User-Friendly Differential Privacy Library in PyTorch
We introduce Opacus, a free, open-source PyTorch library for training deep learning models with differential privacy (hosted at opacus. ai).
Federated Boosted Decision Trees with Differential Privacy
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data.
On the Importance of Difficulty Calibration in Membership Inference Attacks
The vulnerability of machine learning models to membership inference attacks has received much attention in recent years.
Reconciling Security and Communication Efficiency in Federated Learning
Cross-device Federated Learning is an increasingly popular machine learning setting to train a model by leveraging a large population of client devices with high privacy and security guarantees.
Node Classification in Social Networks
When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled.
Social and Information Networks Physics and Society
Iterative Hessian Sketch in Input Sparsity Time
Scalable algorithms to solve optimization and regression tasks even approximately, are needed to work with large datasets.
Learning Graphical Models from a Distributed Stream
We study Bayesian networks, the workhorse of graphical models, and present a communication-efficient method for continuously learning and maintaining a Bayesian network model over data that is arriving as a distributed stream partitioned across multiple processors.
Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for problems such as approximate matrix multiplication, low rank approximation, and regression, primarily for the Euclidean norm $\ell_2$.
Frequency Estimation Under Multiparty Differential Privacy: One-shot and Streaming
We study the fundamental problem of frequency estimation under both privacy and communication constraints, where the data is distributed among $k$ parties.
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms
A common countermeasure against MI attacks is to utilize differential privacy (DP) during model training to mask the presence of individual examples.
PrivBayes： Private Data release via Bayesian networks
Given a dataset D, PRIVBAYES first constructs a Bayesian network N , which (i) provides a succinct model of the correlations among the attributes in D and (ii) allows us to approximate the distribution of data in D using a set P of lowdimensional marginals of D. After that, PRIVBAYES injects noise into each marginal in P to ensure differential privacy, and then uses the noisy marginals and the Bayesian network to construct an approximation of the data distribution in D. Finally, PRIVBAYES samples tuples from the approximate distribution to construct a synthetic dataset, and then releases the synthetic data.
Federated Calibration and Evaluation of Binary Classifiers
We address two major obstacles to practical use of supervised classifiers on distributed private data.
Pruning Compact ConvNets for Efficient Inference
The resulting family of pruned models can consistently obtain better performance than existing FBNetV3 models at the same level of computation, and thus provide state-of-the-art results when trading off between computational complexity and generalization performance on the ImageNet benchmark.
FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
In this work, we initiate the study of federated synthetic tabular data generation.
