Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy

@article{Barroso2020FederatedLA,
  title={Federated Learning and Differential Privacy: Software tools analysis, the Sherpa.ai FL framework and methodological guidelines for preserving data privacy},
  author={Nuria Rodr{\'i}guez Barroso and Goran Stipcich and Daniel Jim'enez-L'opez and Jos'e Antonio Ruiz-Mill'an and Eugenio Mart{\'i}nez-C{\'a}mara and Gerardo Gonz{\'a}lez-Seco and M. Victoria Luz{\'o}n and Miguel Angel Veganzones and Francisco Herrera},
  journal={Inf. Fusion},
  year={2020},
  volume={64},
  pages={270-292}
}
Abstract The high demand of artificial intelligence services at the edges that also preserve data privacy has pushed the research on novel machine learning paradigms that fit these requirements. Federated learning has the ambition to protect data privacy through distributed learning methods that keep the data in its storage silos. Likewise, differential privacy attains to improve the protection of data privacy by measuring the privacy loss in the communication among the elements of federated… Expand
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References

SHOWING 1-10 OF 82 REFERENCES
Federated Learning
TLDR
It is shown how federated learning can become the foundation of next-generation machine learning that caters to technological and societal needs for responsible AI development and application. Expand
Differentially Private Federated Learning: A Client Level Perspective
TLDR
The aim is to hide clients' contributions during training, balancing the trade-off between privacy loss and model performance, and empirical studies suggest that given a sufficiently large number of participating clients, this procedure can maintain client-level differential privacy at only a minor cost in model performance. Expand
Federated Collaborative Filtering for Privacy-Preserving Personalized Recommendation System
TLDR
Empirical validation confirms a collaborative filter can be federated without a loss of accuracy compared to a standard implementation, hence enhancing the user's privacy in a widely used recommender application while maintaining recommender performance. Expand
Federated learning of predictive models from federated Electronic Health Records
TLDR
An iterative cluster Primal Dual Splitting algorithm for solving the large-scale sSVM problem in a decentralized fashion, which extracts important features discovered by the algorithm that are predictive of future hospitalizations, thus providing a way to interpret the classification results and inform prevention efforts. Expand
The Algorithmic Foundations of Differential Privacy
TLDR
The preponderance of this monograph is devoted to fundamental techniques for achieving differential privacy, and application of these techniques in creative combinations, using the query-release problem as an ongoing example. Expand
Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
TLDR
This work describes a three-party end-to-end solution in two phases ---privacy-preserving entity resolution and federated logistic regression over messages encrypted with an additively homomorphic scheme---, secure against a honest-but-curious adversary. Expand
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
TLDR
This work formulates a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs and obtains the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDl learning time, accuracy level, and UE energy cost. Expand
Clustered Federated Learning: Model-Agnostic Distributed Multitask Optimization Under Privacy Constraints
TLDR
Closed FL (CFL), a novel federated multitask learning (FMTL) framework, which exploits geometric properties of the FL loss surface to group the client population into clusters with jointly trainable data distributions, and comes with strong mathematical guarantees on the clustering quality. Expand
Analyzing Federated Learning through an Adversarial Lens
TLDR
This work explores the threat of model poisoning attacks on federated learning initiated by a single, non-colluding malicious agent where the adversarial objective is to cause the model to misclassify a set of chosen inputs with high confidence. Expand
Privacy-Preserving Distributed Linear Regression on High-Dimensional Data
TLDR
A hybrid multi-party computation protocol that combines Yao’s garbled circuits with tailored protocols for computing inner products is proposed, suitable for secure computation because it uses an efficient fixed-point representation of real numbers while maintaining accuracy and convergence rates comparable to what can be obtained with a classical solution using floating point numbers. Expand
...
1
2
3
4
5
...