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.
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.
AdaDKRR tackles data silos by combining autonomy, privacy, and collaboration.
problem Data silos caused by privacy and interoperability constraints.
method Adaptive distributed kernel ridge regression (AdaDKRR) with autonomy, privacy, and collaboration.
result AdaDKRR performs similarly to optimal learning algorithms on the whole data under mild conditions.
Federated Cox model handles non-proportional hazards in siloed data.
problem Handling non-proportional hazards in federated healthcare data.
method Developed a federated Cox model that relaxes proportional hazards assumption.
result Federated model performs similarly to standard models on clinical datasets.
A practical one-shot federated learning algorithm for cross-silo setting.
problem Limited applicability of existing one-shot federated learning algorithms due to specific model support and lack of privacy guarantees.
method FedKT, a one-shot federated learning algorithm that supports any classification models and provides differential privacy guarantees.
result FedKT significantly outperforms other state-of-the-art federated learning algorithms with a single communication round.
Workplace communications became more siloed during the pandemic, reducing stability within communities.
problem Understanding changes in intra-organizational communication networks during the pandemic.
method Analyzed aggregated email metadata from 4,361 organizations worldwide over 24 months.
result Organizations became more siloed in 2020, with decreased stability within silos.
Study federates measurement of demographic disparities from quantile sketches.
problem Misalignment of fairness goals with siloed data collection and privacy regulations.
method Federated auditing of demographic parity through score distributions, using Wasserstein--Frechet variance and quantile summaries.
result Proposes a one-shot, communication-efficient protocol to estimate global disparity and its decomposition.
In the forthcoming ISDA Standard Credit Support Annex (SCSA), the trades denominated in non-G5 currencies as well as those include multiple currencies are expected to be allocated to the USD silo, where the contracts are collateralized by USD cash, or a different currency with an appropriate interest rate overlay to ac…
Federated learning predicts financial distress across U.S. states without centralizing data.
problem Predicting financial distress across U.S. states using sensitive data without centralization.
method Cross-silo federated learning, interpretable AI techniques, machine learning model for categorical data.
result Identifies both global and state-specific predictors of financial hardship.
AAggFF improves federated learning fairness through sequential decision making.
problem Achieving client-level fairness in federated learning systems.
method Unified online convex optimization framework for adaptive aggregation strategies.
result AAggFF achieves better client-level fairness in federated learning.
An online framework improves investment management by making incremental updates.
problem Offline investment processes restrict silos from collectively pursuing a unified goal.
method Developed an online algorithm workflow for portfolio management.
result The online framework outperforms market benchmarks and reduces overfitting.
Federated learning involves training statistical models over remote devices or siloed data centers, such as mobile phones or hospitals, while keeping data localized. Training in heterogeneous and potentially massive networks introduces novel challenges that require a fundamental departure from standard approaches for l…
Combining differential privacy and federated learning improves data security.
problem Ensuring privacy in distributed machine learning.
method Integrating differential privacy with federated learning protocols.
result Achieved comparable prediction accuracy to non-distributed learning while maintaining strict privacy.
Bayesian mixture models inference in federated learning
problem Inference of Bayesian mixture models in federated learning
method Consensus Monte Carlo approach
result Recovery of small clusters with greater accuracy than standard MCMC
Paper proposes FedAMP for improved federated learning with non-IID data.
problem Challenges of federated learning with non-IID data.
method FedAMP: Federated attentive message passing for pairwise client collaborations.
result FedAMP improves federated learning performance with non-IID data.
Paper introduces OARF benchmark suite for federated learning systems.
problem Limited diversity in federated learning benchmarks.
method Characterizes OARF benchmark suite with diverse data and applications.
result Federated learning can effectively increase end-to-end throughput.
Intensive care data are valuable for improvement of health care, policy making and many other purposes. Vast amount of such data are stored in different locations, on many different devices and in different data silos. Sharing data among different sources is a big challenge due to regulatory, operational and security r…
A novel incentive mechanism improves fairness and participation in federated learning.
problem Low-quality clients and lack of fairness in federated learning.
method Client selection process and money transfer mechanism to ensure fairness and participation.
result The proposed incentive mechanism improves the duration and fairness of federated learning.
This paper analyzes privacy threats in federated matrix factorization.
problem Privacy threats in federated matrix factorization models.
method Categorizes federated matrix factorization into three types and analyzes privacy threats.
result This is the first study of privacy threats in federated matrix factorization.
A fair reward system boosts participation in federated learning.
problem Fairness in federated learning among competitive agents with siloed data.
method Hierarchically fair federated learning (HFFL) framework with proportional rewards based on contribution levels.
result Efficacy of HFFL in maintaining fairness and facilitating federated learning in competitive settings.
With the emergence of data silos and popular privacy awareness, the traditional centralized approach of training artificial intelligence (AI) models is facing strong challenges. Federated learning (FL) has recently emerged as a promising solution under this new reality. Existing FL protocol design has been shown to exh…
Paper explores how to design federated learning protocols that benefit all participants while maintaining privacy.
problem Privacy concerns undermine the accuracy benefits of federated learning in privacy-sensitive domains.
method The paper provides conditions for mutually beneficial federated learning protocols and designs protocols that maximize total utility and accuracy.
result The paper demonstrates that federated learning can be designed to be mutually beneficial, striking a balance between privacy and model accuracy.
Ubiquitous systems with End-Edge-Cloud architecture are increasingly being used in healthcare applications. Federated Learning (FL) is highly useful for such applications, due to silo effect and privacy preserving. Existing FL approaches generally do not account for disparities in the quality of local data labels. Howe…
EIGAN learns private representations without centralized data, outperforming state-of-the-art.
problem Private representation learning with multiple ally and adversary attributes.
method Exclusion-Inclusion Generative Adversarial Network (EIGAN) and Distributed EIGAN (D-EIGAN).
result EIGAN and D-EIGAN outperform state-of-the-art methods in accuracy and scalability.
Sherpa.ai framework combines federated learning and differential privacy for edge AI services.
problem Protecting data privacy in edge AI services.
method Holistic federated learning and differential privacy approach with methodological guidelines.
result Demonstrated through classification and regression use cases.
New algorithms tackle statistical heterogeneity in federated learning.
problem Statistical heterogeneity in distributed machine learning models.
method Introduces three novel methods: SuPerFed, AAggFF, and FedEvg.
result Mitigates statistical heterogeneity in federated learning.
New model bridges pricing and reserving for insurance claims.
problem Incomplete claim data due to reporting and settlement delays.
method Develops an occurrence and development model to estimate both claims and premiums.
result Effective resolution of pricing and reserving inconsistencies.
Study evaluates federated learning with differential privacy on MIMIC-III, improving model performance with careful parameter tuning.
problem Training machine learning models on privacy-sensitive data sets locked in healthcare facilities.
method Extensive evaluation of federated and differential privacy techniques on MIMIC-III dataset, analyzing various parameters.
result Careful parameter tuning is crucial for federated learning with differential privacy, especially for data distribution and communication strategies.
Study maps research streams in biodiversity finance, identifies key areas.
problem Biodiversity loss and need for finance to reverse trends.
method Quantitative bibliometric analysis of 189,456 references.
result Identifies eight primary research streams in biodiversity finance.
Big data sets must be carefully partitioned into statistically similar data subsets that can be used as representative samples for big data analysis tasks. In this paper, we propose the random sample partition (RSP) data model to represent a big data set as a set of non-overlapping data subsets, called RSP data blocks,…
Prevents sensitive data generation in diffusion models using labeled and unlabeled data.
problem Generating sensitive data in diffusion models using unlabeled data.
method Positive-Unlabeled Diffusion Models, approximating ELBO with labeled and unlabeled data.
result Prevents the generation of sensitive data without compromising image quality.
Study reveals Data Shapley's inconsistent performance in data selection tasks.
problem Inconsistency of Data Shapley's performance in data selection across different settings.
method Hypothesis testing framework and identification of utility functions.
result Data Shapley's performance is no better than random selection without specific constraints.
PRRO generates synthetic tabular data that improves SL performance and class distribution.
problem Low SL utility of synthetic data due to class imbalance and overlooked data relationships.
method Data pruning and column reordering to optimize SL utility.
result Synthetic data generated with PRRO enhances predictive performance and class distribution.
Defines data science as a natural ecosystem with challenges and missions.
problem Challenges and missions in data science due to 5D complexities and data life cycle phases.
method Systemic and data-centric view of data science as a fusion of data universe and its challenges, formalizing a general-purpose architecture.
result Essential data science as a natural ecosystem integrating specific disciplines and high-impact applications.
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack o…
Synthetic data enhances analytics but requires careful volume management.
problem Accuracy of statistical methods on synthetic data vs. raw data.
method Synthetic Data Generation for Analytics framework using tabular diffusion models.
result Error rate decreases with more synthetic data but may stabilize or increase.
New test ensures quality of shared data in machine learning.
problem Ensuring quality of external data in machine learning tasks.
method Distribution-free two-sample testing procedures grounded in conformal outlier detection.
result Identifies valuable external data agents for model personalization.
Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data. Repeated accesses to underlying data incur increasing loss. Releasing data as privacy-preserving synthetic data would avoid this limitation, but would leave open the problem of designing what kind of synthetic data. W…
Paper creates fair synthetic data ensuring equal predictions across sensitive attributes.
problem Ensuring fair predictions across sensitive attributes in synthetic data.
method Equalizing target probability distributions across sensitive attributes in synthetic data generation.
result Synthetic data provides strong fair predictions, equal across all thresholds.
DPA preserves data distribution in reduced dimensions.
problem Loss of data distribution in dimension reduction.
method DPA combines encoder and decoder to match data distribution.
result DPA successfully reconstructs data distribution.
Efficient synthetic data generation improves model performance on tabular data.
problem Improving model robustness and performance with scarce or low-quality data.
method Hardness characterization to identify high-value training points, generating synthetic data only from these points.
result Synthetic data generated from hardest points outperforms non-targeted methods on tabular datasets.
For most problems in science and engineering we can obtain data sets that describe the observed system from various perspectives and record the behavior of its individual components. Heterogeneous data sets can be collectively mined by data fusion. Fusion can focus on a specific target relation and exploit directly ass…
DAERNN models censored data using neural networks with data augmentation.
problem Handling censored data in expectile regression.
method Data augmentation based Expectile Regression Neural Networks (ERNNs).
result DAERNN outperforms existing censored ERNNs methods and achieves comparable predictive performance to fully observed data.
Data preprocessing techniques are devoted to correct or alleviate errors in data. Discretization and feature selection are two of the most extended data preprocessing techniques. Although we can find many proposals for static Big Data preprocessing, there is little research devoted to the continuous Big Data problem. A…
Data stream classification methods demonstrate promising performance on a single data stream by exploring the cohesion in the data stream. However, multiple data streams that involve several correlated data streams are common in many practical scenarios, which can be viewed as multi-task data streams. Instead of handli…
Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough …
This paper quantifies uncertainty in Data Shapley using statistical inference.
problem Uncertainty in data valuation due to dynamic data distribution.
method Established relationship with U-statistics and quantified uncertainty using statistical inference.
result Confidence intervals for Data Shapley estimations are provided.
DCoM uses deep neural networks to detect semantic data types from raw column values.
problem Detecting semantic data types from dirty and unseen data.
method DCoM employs multi-input NLP-based deep neural networks trained on 686,765 data columns.
result DCoM outperforms existing methods significantly on 78 different semantic data types.