On-device research index

arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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10 results for “cross-silo”

On Privacy and Personalization in Cross-Silo Federated Learningcs.LG

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.

differential privacy federated learning cross-silo personalization privacy regulations

2022-06-16abs ↗pdf ↗

Practical One-Shot Federated Learning for Cross-Silo Settingcs.LG

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.

federated learning one-shot cross-silo privacy differential privacy

2020-10-02abs ↗pdf ↗

Pursuing Overall Welfare in Federated Learning through Sequential Decision Makingcs.LG

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.

federated learning sequential decision making fairness aggregation regret bound

2024-05-31abs ↗pdf ↗

Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Lossescs.LG

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.

federated learning differential privacy private machine learning cross-silo learning convex optimization

2021-06-17abs ↗pdf ↗

Differentially private cross-silo federated learningcs.CR

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.

differential privacy federated learning privacy amplification secure summation subsampled learning

2020-07-10abs ↗pdf ↗

Personalized Cross-Silo Federated Learning on Non-IID Datacs.LG

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.

federated learning non-IID data federated attentive message passing pairwise collaborations

2020-07-07abs ↗pdf ↗

Provable Mutual Benefits from Federated Learning in Privacy-Sensitive Domainsstat.ML

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.

federated learning privacy mutual benefits mean estimation convex optimization

2024-03-11abs ↗pdf ↗

Explainable Federated Learning for U.S. State-Level Financial Distress Modelingq-fin.ST

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.

federated learning financial distress explainable AI consumer credit risk financial inclusion

2025-10-28abs ↗pdf ↗

Federated Measurement of Demographic Disparities from Quantile Sketchesstat.ML

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.

federated learning demographic parity Wasserstein distance quantile sketches ANOVA decomposition

2026-02-21abs ↗pdf ↗

Variational Consensus Monte Carlo for Bayesian Mixturestat.ML

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

Bayesian mixture federated learning consensus Monte Carlo cluster recovery electronic health record

2026-06-17abs ↗pdf ↗