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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48 results for “federated measurement”

Translation invariant curvature measures of convex bodiesmath.DG

New tools study curvature measures of convex bodies, revealing structured spaces.

problem Investigate translation invariant curvature measures of convex bodies.

method Introduce new tools to study curvature measures, proving conjectures about their structure.

result Space of curvature measures has length at most 2 as a representation of the general linear group in degrees 0 and n-2.

curvature measures convex bodies translation invariant general linear group representation theory

2026-01-20abs ↗pdf ↗

FedCM: A Real-time Contribution Measurement Method for Participants in Federated Learningcs.LG

FedCM measures contributions in real-time for federated learning.

problem Fairly allocating contributions in federated learning systems.

method FedCM calculates impact based on current and previous rounds with attention aggregation.

result FedCM is more sensitive to data quality and quantity in real-time.

federated learning contribution measurement attention aggregation real-time fair credits allocation

2020-09-08abs ↗pdf ↗

A V2X-based Privacy Preserving Federated Measuring and Learning Systemcs.LG

Proposes a privacy-preserving system for federated learning of road networks.

problem Privacy and security of data shared between vehicles and infrastructure.

method Federated learning over V2V and V2N links, non-IID dataset modeling.

result Improves learning performance and prevents eavesdropping.

federated learning privacy-preserving V2X federated measurement vehicle-to-vehicle

2024-01-24abs ↗pdf ↗

Measuring Heterogeneity in Machine Learning with Distributed Energy Distancestat.ML

Energy distance measures feature heterogeneity in federated learning.

problem Heterogeneity across data sources hinders model aggregation in federated learning.

method Introduced Taylor approximations of energy distance for efficient computation.

result Taylor approximations accurately capture feature discrepancies, improving convergence.

federated learning feature heterogeneity energy distance Taylor approximations convergence

2025-01-27abs ↗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 ↗

Decision-Focused Federated Learning Under Heterogeneous Objectives and Constraintsmath.OC

DFFL tackles federated learning with heterogeneous objectives and constraints.

problem Federated learning with clients having different objectives and feasible regions.

method Derived heterogeneity bounds for cost-vector distances and support-function/shape-distance terms. Lifted pointwise bounds to local-versus-federated excess-risk comparison.

result Federation is beneficial when the statistical advantage of pooling exceeds a client-specific heterogeneity penalty.

Federated Learning Decision-focused Heterogeneous Objectives Feasible Regions Excess-Risk

2026-04-21abs ↗pdf ↗

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcs.LG

Federated Learning enables visual models to be trained in a privacy-preserving way using real-world data from mobile devices. Given their distributed nature, the statistics of the data across these devices is likely to differ significantly. In this work, we look at the effect such non-identical data distributions has o…

2019-09-13abs ↗pdf ↗

Interpret Federated Learning with Shapley Valuescs.LG

Federated Learning is introduced to protect privacy by distributing training data into multiple parties. Each party trains its own model and a meta-model is constructed from the sub models. In this way the details of the data are not disclosed in between each party. In this paper we investigate the model interpretation…

2019-05-11abs ↗pdf ↗

Ditto: Fair and Robust Federated Learning Through Personalizationcs.LG

Ditto improves fairness and robustness in federated learning.

problem Fairness and robustness in statistically heterogeneous federated learning networks.

method Personalized federated learning framework (Ditto) with a scalable solver.

result Ditto achieves competitive performance and superior fairness and robustness compared to existing methods.

federated learning fairness robustness personalization federated datasets

2020-12-08abs ↗pdf ↗

Measure Contribution of Participants in Federated Learningcs.LG

Federated Machine Learning (FML) creates an ecosystem for multiple parties to collaborate on building models while protecting data privacy for the participants. A measure of the contribution for each party in FML enables fair credits allocation. In this paper we develop simple but powerful techniques to fairly calculat…

2019-09-17abs ↗pdf ↗

A Hybrid Federated Kernel Regularized Least Squares Algorithmstat.ML

A new hybrid federated learning algorithm for combining clinical and omics data.

problem Combining clinical and omics data in federated learning settings.

method Reformulated Kernel Regularized Least Squares algorithm for hybrid federated learning.

result Validation of two variants of the hybrid algorithm on well-established datasets.

federated learning kernel regularized least squares hybrid setting clinical data omics data

2024-07-24abs ↗pdf ↗

A Federated F-score Based Ensemble Model for Automatic Rule Extractioncs.LG

Fed-FEARE model extracts rules from multiple agencies' data securely.

problem Data privacy and secure rule extraction across multiple agencies.

method Federated F-score based ensemble tree model.

result Model performance measures significantly improved with federated learning.

federated learning rule extraction privacy protection ensemble model financial institutions

2020-07-07abs ↗pdf ↗

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

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.

federated learning differential privacy Sherpa.ai edge AI privacy protection

2020-07-02abs ↗pdf ↗

Federated UCBVI: Communication-Efficient Federated Regret Minimization with Heterogeneous Agentscs.LG

Federated UCBVI reduces communication costs while minimizing regret in multi-agent settings.

problem Minimizing regret in federated learning with heterogeneous agents.

method Federated Upper Confidence Bound Value Iteration (Fed-UCBVI) algorithm.

result Regret bound scales as

ilde{\mathcal{O}}(\sqrt{H^3 |\mathcal{S}| |\mathcal{A}| T / M})

with small additional term due to heterogeneity.

Federated Learning Reinforcement Learning UCBVI Heterogeneity Communication Efficiency

2024-10-30abs ↗pdf ↗

Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecs.LG

New algorithm reduces federated learning rounds and improves privacy.

problem Inefficient synchronous federated learning causing scalability issues.

method Asynchronous federated learning with reduced communication and differential privacy via Gaussian noise.

result The algorithm reduces waiting times and network communication, making federated learning more scalable and private.

federated learning asynchronous privacy differential privacy Gaussian noise

2020-07-17abs ↗pdf ↗

FedGTEA: Federated Class-Incremental Learning with Gaussian Task Embedding and Alignmentcs.LG

FedGTEA learns new tasks in federated learning with task embeddings and alignment.

problem Federated class-incremental learning with task-specific knowledge and model uncertainty.

method Cardinality-Agnostic Task Encoder (CATE) for Gaussian task embeddings, 2-Wasserstein distance for inter-task alignment.

result FedGTEA achieves superior classification performance and mitigates forgetting.

federated learning class-incremental learning task embeddings 2-Wasserstein distance privacy

2025-10-14abs ↗pdf ↗

Semi-Supervised Federated Multi-Label Feature Selection with Fuzzy Information Measurescs.LG

A federated method for feature selection in multi-label data.

problem Feature selection in multi-label data for distributed and federated environments.

method Semi-Supervised Federated Multi-Label Feature Selection (SSFMLFS) using fuzzy information measures.

result SSFMLFS outperforms other methods in feature selection for multi-label data in federated settings.

feature selection federated learning multi-label fuzzy information PageRank

2025-11-21abs ↗pdf ↗

Quality Inference in Federated Learning with Secure Aggregationcs.LG

Federated learning leaks participant dataset quality even with secure aggregation.

problem Leakage of participant dataset quality in federated learning with secure aggregation.

method Image recognition experiments to infer and attribute dataset quality.

result Relative quality ordering of participants can be inferred and used for various purposes.

Federated Learning Secure Aggregation Dataset Quality Privacy Leakage Misbehavior Detection

2020-07-13abs ↗pdf ↗

A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcs.LG

This paper evaluates and compares gradient leakage attacks in federated learning.

problem Gradient leakage attacks compromise client privacy in federated learning.

method Formal and experimental analysis of gradient leakage attacks, evaluation of attack effectiveness and cost.

result Gradient leakage attacks can reconstruct private local training data from shared parameter updates.

federated learning gradient leakage privacy machine learning privacy leakage

2020-04-22abs ↗pdf ↗

Communication-Efficient Federated Risk Difference Estimation for Time-to-Event Clinical Outcomesstat.ML

FedRD improves risk difference estimation in federated learning for clinical outcomes.

problem Privacy-preserving model co-training in medical research is hindered by server-dependent architectures and focus on relative effect measures.

method FedRD is a server-independent, communication-efficient framework for federated risk difference estimation in distributed survival data.

result FedRD provides valid confidence intervals and hypothesis testing, and is asymptotically equivalent to pooled individual-level analysis.

federated learning risk difference clinical outcomes privacy-preserving time-to-event

2026-01-21abs ↗pdf ↗

Symplectic Harmonic theory and the Federer-Fleming deformation theoremmath.SG

In this article, we initiate a geometric measure theoretic approach to symplectic Hodge theory. In particular, we apply one of the central results in geometric measure theory, the Federer-Fleming deformation theorem, together with the cohomology theory of normal cur- rents on a differential manifold, to establish a fun…

2011-12-12abs ↗pdf ↗

Personalized Federated Learning: A Meta-Learning Approachcs.LG

Personalized federated learning adapts models to each user's data.

problem Training models across users without data exchange leads to a common model, not personalized.

method Develops a personalized federated learning approach using Model-Agnostic Meta-Learning (MAML).

result Personalized models can be adapted by a few gradient descent steps on local data.

Federated Learning Meta-Learning Personalized Models Gradient Descent Model Agnostic

2020-02-19abs ↗pdf ↗

GraMFedDHAR: Graph Based Multimodal Differentially Private Federated HARcs.LG

Graph-based multimodal federated learning for HAR improves accuracy and privacy.

problem Challenges in HAR due to noisy data, incomplete measurements, and privacy concerns.

method Proposes GraMFedDHAR, a Graph-based Multimodal Federated Learning framework for HAR tasks, using modality-specific graphs, residual GCNs, and attention-based fusion.

result Experimental results show up to 13 percent performance improvement for MultiModalGCN under differential privacy constraints.

Human Activity Recognition Multimodal Sensor Data Federated Learning Graph Convolutional Neural Networks Differential Privacy

2025-09-06abs ↗pdf ↗

Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Modelscs.LG

This work analyzes aggregation strategies for Bayesian deep learning models in federated learning.

problem Improper aggregation of Bayesian deep learning models in federated learning leads to sub-optimal performance.

method Six aggregation strategies for Bayesian deep learning models are analyzed using CIFAR-10 dataset and a fully variational ResNet-20 architecture.

result Aggregation strategy is a key hyperparameter affecting accuracy, calibration, uncertainty quantification, training stability, and client compute requirements.

federated learning Bayesian deep learning aggregation strategies CIFAR-10 ResNet-20

2024-03-22abs ↗pdf ↗

Besicovitch-Federer projection theorem and geodesic flows on Riemann surfacesmath.CA

We extend the Besicovitch-Federer projection theorem to transversal families of mappings. As an application we show that on a certain class of Riemann surfaces with constant negative curvature and with boundary, there exist natural 2-dimensional measures invariant under the geodesic flow having 2-dimensional supports s…

2011-04-18abs ↗pdf ↗

Federated Learning for Heterogeneous Bandits with Unobserved Contextscs.LG

A federated learning algorithm tackles unknown contexts in multi-arm bandits.

problem Learning optimal actions in federated multi-arm bandits with unobserved contexts.

method Elimination-based algorithm for linearly parametrized reward functions.

result Proved regret bound for linearly parametrized reward functions.

federated learning multi-arm bandits unknown contexts regret bound elimination-based algorithm

2023-03-29abs ↗pdf ↗

A Hierarchical Sampling Framework for bounding the Generalization Error of Federated Learningcs.LG

Hierarchical Federated Learning bounds generalize using Wasserstein distance.

problem Bounding generalization error in Federated Learning with hierarchical sampling.

method Introduced a hierarchical sampling framework and derived generalization bounds using Wasserstein distance.

result Recover and strictly imply existing CMI bounds for bounded losses.

Federated Learning Hierarchical Sampling Generalization Error Wasserstein Distance Conditional Mutual Information

2026-05-05abs ↗pdf ↗

Towards Uncertainty-Aware Federated Granger Causal Learningcs.LG

Federated Granger causality learns reliable interactions without sharing data.

problem Uncertainty in federated Granger causality estimates.

method Closed-form covariance recursions and spectral-radius-based convergence conditions.

result Uncertainty depends only on client data statistics and is independent of model parameters.

Federated Learning Granger Causality Uncertainty Quantification Causal Structure Learning Spectral Radius

2026-02-13abs ↗pdf ↗

Anchor Sampling for Federated Learning with Partial Client Participationcs.LG

FedAMD framework improves federated learning with partial client participation.

problem Data heterogeneity and inactive client updates in partial client participation.

method Anchor sampling divides clients into anchor and miner groups, using large and small batches respectively.

result FedAMD achieves faster convergence and improved model performance compared to state-of-the-art methods.

federated learning partial participation anchor sampling data heterogeneity gradient computation

2022-06-13abs ↗pdf ↗

Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcs.LG

MTFL improves UA and speeds convergence in personalised DNNs on edge devices.

problem Non-IID user data harms FL convergence and global UA is not always the goal.

method Introduces non-federated BN layers into federated DNNs for personalised training.

result MTFL reduces UA rounds by up to 5x and convergence time by up to 3x.

Federated Learning Personalized Deep Neural Networks Edge Computing Batch-Normalization Convergence Speed

2020-07-17abs ↗pdf ↗

Federated Uncertainty-Aware Learning for Distributed Hospital EHR Datacs.LG

Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and…

2019-10-27abs ↗pdf ↗

Communication-Efficient Federated Distillationcs.LG

Compressed Federated Distillation reduces communication in federated learning.

problem Communication constraints in Federated Learning.

method Compressed Federated Distillation (CFD) leverages soft labels and quantization techniques.

result Reduces communication by more than 4 orders of magnitude compared to Federated Averaging.

Federated Learning Communication Efficiency Federated Distillation Soft Labels Quantization

2020-12-01abs ↗pdf ↗

Towards the Theory of Unsupervised Federated Learning: Non-asymptotic Analysis of Federated EM Algorithmsstat.ML

This paper develops a federated EM algorithm for unsupervised learning of mixture models.

problem Theoretical foundations of unsupervised federated learning are lacking.

method Introduces a federated gradient EM algorithm (FedGrEM) for unsupervised learning of mixture models.

result Theoretical analysis shows FedGrEM outperforms local single-task learning.

federated learning unsupervised learning federated EM mixture models finite-sample theory

2023-10-23abs ↗pdf ↗

Adaptive Federated Optimizationcs.LG

Adaptive optimizers improve federated learning performance.

problem Inadequate optimization methods in federated learning.

method Federated versions of adaptive optimizers (Adagrad, Adam, Yogi).

result Adaptive optimizers enhance federated learning performance.

federated learning adaptive optimization federated Averaging non-convex optimization client heterogeneity

2020-02-29abs ↗pdf ↗

Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environmentscs.LG

Federated multi-mini-batch improves efficiency in non-IID environments.

problem Performance and communication efficiency challenges in federated learning with non-IID data.

method Introduces federated multi-mini-batch approach to balance performance and communication.

result Federated multi-mini-batch outperforms federated averaging in non-IID settings.

Federated Learning Non-IID Data Mini-Batch Communication Efficiency

2020-11-13abs ↗pdf ↗

Handling Covariate Mismatch in Federated Linear Predictionmath.ST

Study addresses covariate mismatch in federated learning, improving model accuracy.

problem Learning from clients with different feature sets in federated learning.

method Developed two approaches for linear prediction under covariate mismatch: plug-in estimator and impute-then-regress strategy.

result Proposed methods provide asymptotic and finite-sample learning rates, improving model accuracy.

federated learning covariate mismatch linear prediction ridge regularization asymptotic analysis

2026-02-02abs ↗pdf ↗

Federated Instrumental Variable Analysis via Federated Generalized Method of Momentscs.LG

FedIV uses federated GMM for IV analysis in non-i.i.d. data.

problem Efficient IV analysis in non-i.i.d. federated data.

method Federated GMM via FedGDA algorithm.

result Federated solution consistently estimates local moment conditions.

federated learning instrumental variables GMM non-i.i.d. data deep learning

2025-05-27abs ↗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 ↗

Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcs.LG

Paper tackles concept drift in Federated Learning, improving model performance.

problem Concept drift in real-world data makes existing Federated Learning methods ineffective.

method Introduces a multiscale algorithm combining extit{FedAvg} and extit{FedOMD} with non-stationary detection and adaptation.

result Achieves dynamic regret of $\Tilde{\mathcal{O}} ( \min \{ \sqrt{LT} , Δ^{\frac{1}{3}}T^{\frac{2}{3}} + \sqrt{T} \})$ for

T

rounds.

Federated Learning Concept Drift Non-Stationary Detection Adaptation Dynamic Regret

2022-11-22abs ↗pdf ↗

The OARF Benchmark Suite: Characterization and Implications for Federated Learning Systemscs.LG

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.

federated learning benchmark suite federated machine learning throughput convergence time

2020-06-14abs ↗pdf ↗

Federated

f

-Differential Privacystat.ML

New federated $f$ -differential privacy for collaborative learning.

problem Privacy in federated learning.

method Introducing federated

f

-differential privacy and proposing a generic private federated learning framework.

result Proves federated

f

-differential privacy provides privacy guarantee on each record of one client's data.

federated learning privacy differential privacy f-differential privacy collaborative learning

2021-02-22abs ↗pdf ↗

Stochastic Smoothed Gradient Descent Ascent for Federated Minimax Optimizationstat.ML

New algorithm solves federated minimax optimization problems.

problem Federated minimax optimization challenges.

method Federated Stochastic Smoothed Gradient Descent Ascent (FESS-GDA).

result FESS-GDA uniformly solves federated minimax problems.

federated learning minimax optimization smoothed gradient ascent FESS-GDA convergence

2023-11-02abs ↗pdf ↗

Federated Asymptotics: a model to compare federated learning algorithmscs.LG

A framework to compare federated learning algorithms in high-dimensional settings.

problem Comparing the performance of federated learning algorithms in high-dimensional settings.

method Formulating federated learning as a multi-criterion objective and analyzing a linear regression model.

result Federated Averaging with simple client fine-tuning achieves the same asymptotic risk as more intricate approaches and outperforms without personalization.

federated learning asymptotics personalization information sharing meta-learning

2021-08-16abs ↗pdf ↗

An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effectscs.LG

Adaptive kernel approach learns causal effects from diverse data sources.

problem Learning causal effects from multiple, decentralized data sources in a federated setting.

method Adaptive transfer algorithm using Random Fourier Features to estimate similarities and disentangle loss function components.

result Empirically outperforms baselines on decentralized data sources with different distributions.

federated learning categorical effects heterogeneous data privacy minimax bounds

2023-01-01abs ↗pdf ↗

A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncs.LG

Federated learning has been a hot research topic in enabling the collaborative training of machine learning models among different organizations under the privacy restrictions. As researchers try to support more machine learning models with different privacy-preserving approaches, there is a requirement in developing s…

2019-07-23abs ↗pdf ↗

A Non-parametric View of FedAvg and FedProx: Beyond Stationary Pointsstat.ML

Paper analyzes FedAvg and FedProx, showing they don't reach global optima and may not generalize well.

problem Federated Learning algorithms fail to reach global optima and may not generalize well in heterogeneous settings.

method Non-parametric regression in RKHS, analyzing convergence and error rates.

result FedAvg and FedProx achieve optimal error rates in certain heterogeneous settings.

Federated Learning Federated Optimization RKHS Non-parametric Stationary Points

2021-06-29abs ↗pdf ↗

Generalization in Federated Learning: A Conditional Mutual Information Frameworkstat.ML

This work analyzes generalization in federated learning using information theory.

problem Generalization performance in federated learning is less explored compared to centralized learning.

method The work applies an information-theoretic analysis via the conditional mutual information (CMI) framework to study federated learning's two-level generalization.

result The work derives multiple CMI-based bounds, including hypothesis-based CMI bounds and fast-rate evaluated CMI bounds, which improve convergence rates for specific model aggregation strategies and structured loss functions.

Federated Learning Generalization Conditional Mutual Information Privacy Preserving Convergence Rate

2025-03-06abs ↗pdf ↗

Sharing Models or Coresets: A Study based on Membership Inference Attackcs.LG

Study compares federated learning and coreset approaches for privacy in distributed machine learning.

problem Measuring privacy in distributed machine learning approaches.

method Comparison of federated learning and coreset approaches using membership inference attack.

result Federated learning offers better privacy than coreset, but with higher communication cost.

distributed machine learning federated learning coresets privacy membership inference attack

2020-07-06abs ↗pdf ↗