New bounds for LDP with heterogeneous privacy levels guaranteeing high probability of accuracy.
problem Statistical estimation under LDP with users having varying privacy levels.
method Developed finite sample upper bounds in ℓ_2-norm with high probability, complemented by lower bounds.
result Optimal guarantees for heterogeneous LDP in terms of probability and constants.
Estimates population mean from user-level data with privacy, accounting for heterogeneity.
problem Heterogeneous user data with varying numbers of data points and distributions.
method Simple model of heterogeneous user data, differential privacy mechanism for estimation.
result Asymptotic optimality of the proposed estimator and general lower bounds on error.
This work optimizes mean estimation under varying privacy constraints.
problem Mean estimation with heterogeneous privacy constraints.
method Proposes an algorithm for mean estimation under different privacy levels for users.
result Shows a saturation phenomenon in performance as privacy levels are relaxed.
This work optimizes mean estimation under varying user privacy demands.
problem Mean estimation with heterogeneous privacy levels.
method Proposes an algorithm that is minimax optimal and has near-linear run-time.
result Privacy requirements of the most stringent users dictate overall error rates.
TransNet improves community detection on target networks using privacy-preserved source networks.
problem Community detection on sensitive network data with privacy constraints.
method Spectral clustering framework leveraging locally distributed privacy-preserved auxiliary networks via randomized response and adaptive weighting.
result TransNet delivers strong gains in community detection across various privacy levels and heterogeneity patterns.
Study improves privacy in cross-silo federated learning by personalizing data.
problem Privacy concerns in cross-silo federated learning.
method Introduced sample-level differential privacy for silos, analyzed mean-regularized multi-task learning.
result Mean-regularized multi-task learning is a strong baseline for cross-silo federated learning under stronger privacy requirements.
Paper extends FFT-based differential privacy method to heterogeneous compositions.
problem Computing accurate differential privacy guarantees for mixed mechanisms.
method Uses Fast Fourier Transform (FFT) for error analysis and parameter selection.
result Provides tighter bounds for heterogeneous compositions compared to homogeneous cases.
Paper tackles federated learning with privacy, enhancing target data analysis.
problem Heterogeneity and privacy of distributed data in federated learning.
method Formulates federated differential privacy, studies statistical problems under privacy constraints.
result Federated differential privacy offers a balance between privacy and knowledge transfer.
FedDAG learns DAG structures from decentralized data.
problem Learning DAG structures from private decentralized data.
method Gradient-based framework with two-level structures.
result FedDAG learns DAG structure without direct data sharing.
Paper tackles nonparametric classification with privacy constraints, achieving optimal accuracy.
problem Nonparametric classification under distributed differential privacy constraints.
method Minimax and adaptive transfer learning, considering privacy, sample sizes, and heterogeneity.
result Developed an adaptive classifier achieving optimal misclassification rate with privacy constraints.
Generative framework unifies and improves personalized learning and estimation methods.
problem Statistical heterogeneity in client data motivates personalized learning models.
method Generative framework unifies and suggests new personalized learning and estimation algorithms.
result AdaPeD algorithm numerically outperforms known algorithms.
FedReLa: A novel data-level approach for imbalanced federated learning
problem Improving accuracy of federated learning models under class imbalance and data heterogeneity
method Re-labeling samples with a feature-dependent label re-allocator
result Significant improvements in accuracy for minority classes and overall accuracy on stepwise-imbalanced and long-tailed datasets
New method stabilizes private LASSO for high-dimensional data with diverse covariate scales.
problem Privacy constraints and heterogeneity in covariate scales degrade LASSO stability and accuracy.
method Gram-based anisotropic objective perturbation to counteract covariate structure.
result Significantly improves convergence and statistical efficiency of private LASSO estimators.
A new algorithm optimizes local objectives in federated learning with heterogeneous clients.
problem Optimizing local objectives in federated learning with heterogeneous client data.
method Proposes PF-PNE algorithm with double elimination strategy.
result PF-PNE algorithm optimizes local objectives with arbitrary heterogeneity and protects client data confidentiality.
The paper introduces a privacy-preserving method for estimating treatment effects that maintains accuracy.
problem Estimating heterogeneous treatment effects in sensitive data while protecting privacy.
method A general meta-algorithm for CATE estimation with differential privacy guarantees, using sample splitting and parallel composition.
result The meta-algorithm maintains accuracy even with differential privacy, showing that most accuracy loss is due to variance increase.
Combines public and private data for better statistical estimation.
problem Estimating aggregate statistics from mixed data with varying privacy needs.
method Mixed estimators optimized for minimizing variance or median, using differential privacy techniques.
result Our mechanisms often outperform baseline methods in empirical tests.
With the proliferation of training data, distributed machine learning (DML) is becoming more competent for large-scale learning tasks. However, privacy concerns have to be given priority in DML, since training data may contain sensitive information of users. In this paper, we propose a privacy-preserving ADMM-based DML…
New method combines regional HIV prevention trial data without sharing individual patient info.
problem Regional differences in HIV prevention efficacy, privacy concerns, and data sharing limitations.
method Federated learning approach that combines site-specific estimators via L1-regularization.
result Improved precision in estimating region-specific survival curves.
We establish the first benchmark for federated learning with differential privacy in ASR, achieving competitive performance.
problem Challenges in training large transformer models for ASR in federated learning with differential privacy.
method Per-layer clipping and layer-wise gradient normalization to mitigate gradient heterogeneity.
result FL with DP is viable in ASR with strong privacy guarantees, achieving competitive performance.
Paper proposes federated offline RL for personalized medicine.
problem Privacy constraints and heterogeneity in healthcare data.
method Multi-site Markov decision process model and first federated policy optimization algorithm.
result The proposed algorithm achieves comparable suboptimality to centralized RL.
Proposes a method for private aggregation in heterogeneous federated learning.
problem Ensuring resilience to Byzantine clients and maintaining client data privacy in federated learning with heterogeneous data.
method Careful co-design of verifiable secret sharing, secure aggregation, and private information retrieval scheme.
result Achieves information-theoretic privacy guarantees and Byzantine resilience under data heterogeneity.
Tree-based model averaging improves CATE estimation from diverse sites.
problem Limited sample size and privacy concerns prevent accurate personalized treatment effect estimation.
method Tree-based model averaging approach to estimate CATEs from multiple heterogeneous sites.
result Improved accuracy in estimating conditional average treatment effects (CATEs) across sites.
Proposes PFWCP for multi-agent tasks with privacy and validity guarantees.
problem Challenges in uncertainty quantification for multi-agent settings.
method Personalized federated weighted conformal prediction (PFWCP) combining local density ratio weighting and weighted quantile aggregation.
result Asymptotically valid coverage guarantees for each agent in heterogeneous settings.
fedCI and fedCI-IOD enable federated causal discovery across diverse datasets with privacy and power enhancements.
problem Causal discovery across multiple datasets with privacy constraints and heterogeneity.
method federated conditional independence test (fedCI) and Integration of Overlapping Datasets (IOD) algorithm extension (fedCI-IOD).
result fedCI-IOD achieves comparable performance to fully pooled analyses, enhancing statistical power and privacy.
New DP methods for estimating means and frequencies with varying privacy demands.
problem Estimating statistics with users having different privacy requirements.
method Proposes algorithms for empirical mean and frequency estimation under heterogeneous privacy constraints, considering both correlated and permuted datasets.
result Establishes theoretical performance guarantees for algorithms, achieving minimax optimality.
Paper studies optimal federated learning for nonparametric regression with privacy constraints.
problem Federated learning for nonparametric regression with heterogeneous differential privacy constraints.
method Proposes distributed privacy-preserving estimators and investigates their risk properties.
result Establishes matching minimax lower bounds for global and pointwise estimation.
Paper improves differential privacy SGD by considering data heterogeneity.
problem Improving differential privacy in machine learning with varying data contributions.
method Introducing influence function to quantify data contributions, designing PIDP-SGD algorithm.
result PIDP-SGD significantly improves machine learning model performance.
This paper improves privacy in federated learning without a trusted server.
problem Privacy in federated learning with silos that distrust each other.
method Introduces Inter-Silo Record-Level Differential Privacy (ISRL-DP) and accelerated algorithms for convex and smooth losses.
result Achieves optimal privacy and accuracy tradeoffs in federated learning.
Framework for multi-task learning with semiparametric models and nuisance parameters.
problem Improving parameter estimation from diverse, heterogeneous datasets.
method Late fusion multi-task learning framework with two-step process: individual task learning followed by adaptive aggregation.
result The method achieves faster convergence rates compared to individual task learning when tasks share similar parametric components.
Introduces six levels of privacy for financial synthetic data.
problem Privacy risks in financial synthetic data.
method Defines six levels of privacy protection for synthetic data.
result Provides a framework to assess and improve privacy in synthetic data.
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 sett…
This paper analyzes user-level local differential privacy in distributed systems.
problem The relationship between user-level and item-level local differential privacy under the local model is complex.
method The paper analyzes the mean estimation problem and applies it to stochastic optimization, classification, and regression. It proposes adaptive strategies to achieve optimal performance at all privacy levels.
result The proposed methods are minimax optimal up to logarithmic factors and show that user-level DP can lead to faster convergence rates than item-level DP.
2020 Census uses more noise to protect privacy than needed, improving data accuracy.
problem Ensuring privacy in census data while maintaining accuracy for policy decisions.
method Applied f f f -differential privacy to track and reduce noise across geographical levels. result The 2020 Census provides stronger privacy protections than its nominal guarantees suggest.
Conformal-DP improves differential privacy on manifold data by calibrating perturbations based on local densities.
problem Lack of density-awareness in existing differential privacy mechanisms for manifold data leads to biased and suboptimal privacy-utility trade-offs.
method Proposes Conformal-DP, a density-aware differential privacy mechanism using conformal transformations to calibrate perturbations based on local densities.
result Demonstrates improved privacy-utility trade-off in heterogeneous data distribution settings compared to state-of-the-art mechanisms.
Federated survival analysis outperforms local and centralized training, with RSF offering the best balance of discrimination, calibration, and robustness.
problem Survival analysis models require large, diverse cohorts but are limited by privacy regulations and lack of centralized data.
method Federated learning (FL) is used to train shared models without exchanging raw data.
result FL consistently outperforms local training and approaches, and occasionally exceeds centralized performance.
GRAND ensures node-level differential privacy for network data.
problem Lack of node-level differential privacy for network data.
method Proposes GRAND, the first mechanism for releasing networks with node-level differential privacy and preserving structural properties.
result GRAND releases networks while ensuring node-level differential privacy and preserving structural properties.
Study privacy vs. utility in estimating network parameters with aggregated data.
problem Privacy-preserving estimation of network parameters from aggregated node degrees.
method β model, local and central differential privacy, minimax lower bounds, simple estimators.
result Achieved minimax-optimal risk bounds for parameter estimation under privacy constraints.
Research shows collective learning across diverse environments is hard due to privacy and security concerns.
problem Privacy, security, and equity concerns restrict information sharing in diverse AI environments.
method Characterized learning algorithms as choice correspondences, provided minimum requirements for rational learning algorithms.
result The only rational learning algorithm in heterogeneous environments is unilaterally learning from a single environment without information sharing.
FLORAS uses orthogonal sequences for SISO FL, offering both DP and convergence guarantees.
problem Privacy-preserving wireless federated learning in SISO systems.
method Leverages orthogonal sequences to eliminate CSIT requirement and provide DP guarantees.
result FLORAS achieves a smooth tradeoff between convergence rate and DP levels.
Improves privacy guarantees by analyzing randomness in privacy-preserving mechanisms.
problem Balancing user privacy and business constraints in privacy-preserving mechanisms.
method Analyzes explicit and implicit randomness in privacy mechanisms and proposes a probabilistic calibration method.
result Proposes privacy at risk, providing stronger privacy guarantees with quantifiable risks.
New algorithms protect user-level privacy in learning tasks.
problem Protecting user-level privacy in learning tasks with stringent constraints.
method Proposes algorithms for learning tasks under user-level differential privacy constraints.
result Privacy cost decreases as users provide more samples or as the number of users increases.
New privacy mechanism for user-level discrete distributions with reduced penalty.
problem Achieving privacy for all items of a single user in practical applications.
method Study of learning discrete distributions with user-level differential privacy, proposing a new mechanism with reduced privacy penalty.
result Proposed mechanism reduces privacy penalty to i l d e O ( k / ( m α 2 ) + k / m ε α ) ilde{\mathcal{O}}(k/(mα^2) + k/\sqrt{m}εα) i l d e O ( k / ( m α 2 ) + k / m ε α ) , significantly smaller than standard mechanisms. Study creates a multimodal learning framework for CVD risk prediction.
problem Predicting cardiovascular disease risk in diverse populations.
method Combines cross modal transformers, graph neural networks, and causal representation learning.
result Model predicts personalized CVD risk with causal invariance across subpopulations.
Novel mean estimation method under user-level differential privacy reduces noise in continual mean estimates.
problem Maintaining accurate running mean estimates under user-level differential privacy.
method Developed a novel mean estimation specific factorization under approximate differential privacy.
result Achieved asymptotically lower mean-squared error bounds in continual mean estimation.
Proposes SROF for row-wise fusion in federated learning for multivariate responses.
problem Heterogeneous client models with shared variable-level structure.
method Sparse Row-wise Fusion (SROF) regularizer and RowFed algorithm.
result Empirically shows consistent error reduction and stronger variable-level cluster recovery.
Paper studies user-level differential privacy in federated linear contextual bandits.
problem Federated learning with user-level differential privacy constraints.
method Unified federated bandits framework, CDP and LDP definitions, ROBIN algorithm.
result Near-optimal learning under user-level CDP with privacy budget and number of clients.
Federated learning improves CRC grading accuracy and privacy.
problem Inter-observer variability and data privacy in CRC grading.
method Multi-scale federated learning framework integrating ResNetRS50.
result Framework achieves 83.5% accuracy, outperforming centralized models.
This work generates synthetic EHRs with privacy guarantees for machine learning tasks.
problem Privacy concerns and heterogeneity in EHR data limit their use in machine learning.
method Generative Adversarial Networks (GANs) with differential privacy (DP) for synthetic data generation.
result Synthetic EHRs maintain performance close to real data, even with DP applied.