19 research outputs found
Federated Learning with Bayesian Differential Privacy
We consider the problem of reinforcing federated learning with formal privacy
guarantees. We propose to employ Bayesian differential privacy, a relaxation of
differential privacy for similarly distributed data, to provide sharper privacy
loss bounds. We adapt the Bayesian privacy accounting method to the federated
setting and suggest multiple improvements for more efficient privacy budgeting
at different levels. Our experiments show significant advantage over the
state-of-the-art differential privacy bounds for federated learning on image
classification tasks, including a medical application, bringing the privacy
budget below 1 at the client level, and below 0.1 at the instance level. Lower
amounts of noise also benefit the model accuracy and reduce the number of
communication rounds.Comment: Accepted at 2019 IEEE International Conference on Big Data (IEEE Big
Data 2019). 10 pages, 2 figures, 4 tables. arXiv admin note: text overlap
with arXiv:1901.0969
A Novel Privacy-Preserved Recommender System Framework based on Federated Learning
Recommender System (RS) is currently an effective way to solve information
overload. To meet users' next click behavior, RS needs to collect users'
personal information and behavior to achieve a comprehensive and profound user
preference perception. However, these centrally collected data are
privacy-sensitive, and any leakage may cause severe problems to both users and
service providers. This paper proposed a novel privacy-preserved recommender
system framework (PPRSF), through the application of federated learning
paradigm, to enable the recommendation algorithm to be trained and carry out
inference without centrally collecting users' private data. The PPRSF not only
able to reduces the privacy leakage risk, satisfies legal and regulatory
requirements but also allows various recommendation algorithms to be applied
A Probabilistic Framework for Modeling the Variability Across Federated Datasets of Heterogeneous Multi-View Observations
International audienceWe propose a novel federated learning paradigm to model data variability among heterogeneous clients in multi-centric studies. Our method is expressed through a hierarchical Bayesian latent variable model, where client-specific parameters are assumed to be realization from a global distribution at the master level, which is in turn estimated to account for data bias and variability across clients. We show that our framework can be effectively optimized through expectation maximization over latent master's distribution and clients' parameters. We tested our method on the analysis of multi-modal medical imaging data and clinical scores from distributed clinical datasets of patients affected by Alzheimer's disease. We demonstrate that our method is robust when data is distributed either in iid and non-iid manners: it allows to quantify the variability of data, views and centers, while guaranteeing high-quality data reconstruction as compared to the state-of-the-art autoencoding models and federated learning schemes