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

Trend · papers per month

count % of corpus

11 results for “Client-Drift”

Communication-Efficient and Drift-Robust Federated Learning via Elastic Netcs.LG

FedElasticNet reduces communication costs and handles client drift in FL.

problem Expensive communication costs and client drift issues in federated learning.

method Leverages elastic net regularizers to sparsify local updates and limit client drift.

result FedElasticNet effectively resolves communication cost and client drift problems.

federated learning elastic net communication efficiency client drift sparsity

2022-10-06abs ↗pdf ↗

SCAFFOLD: Stochastic Controlled Averaging for Federated Learningcs.LG

SCAFFOLD improves Federated Learning by reducing client-drift and speeding up convergence.

problem Federated Learning's performance is limited by client-drift in heterogeneous data.

method SCAFFOLD uses control variates to correct client-drift and improve convergence.

result SCAFFOLD requires fewer communication rounds and is not affected by data heterogeneity.

Federated Learning Control Variates Client-Drift Variance Reduction Convergence Rates

2019-10-14abs ↗pdf ↗

Mime: Mimicking Centralized Stochastic Algorithms in Federated Learningcs.LG

Mime algorithm improves federated learning by adapting centralized methods.

problem Mitigating client drift in federated learning.

method Combines control variates and server-level statistics to adapt centralized algorithms to federated learning.

result Mime outperforms any centralized method in federated learning.

federated learning client drift control variates momentum variance reduction

2020-08-08abs ↗pdf ↗

Group Personalized Federated Learningstat.ML

Group personalization improves FL performance in heterogeneous client data.

problem Mitigating client drift in federated learning with heterogeneous data.

method Fine-tuning a global FL model over homogeneous groups of clients, then personalizing each group's model.

result The proposed method achieves superior personalization performance compared to other FL approaches.

federated learning personalized learning heterogeneous data client drift Bayesian hierarchical modeling

2022-10-04abs ↗pdf ↗

FedPop: A Bayesian Approach for Personalised Federated Learningcs.LG

Proposes FedPop for personalised federated learning with uncertainty quantification.

problem Uncertainty quantification and client drift in personalised federated learning.

method FedPop recasts FL into population modeling with Markov chain Monte Carlo methods.

result Non-asymptotic convergence guarantees for uncertainty quantification.

federated learning personalized uncertainty quantification Markov chain Monte Carlo client drift

2022-06-07abs ↗pdf ↗

Federated Averaging Langevin Dynamics: Toward a unified theory and new algorithmsstat.ML

Paper improves Bayesian inference in federated learning with new algorithm VR-FALD*.

problem Bayesian inference in federated learning with communication bottlenecks and statistical heterogeneity.

method Federated Averaging Langevin Dynamics (FALD) and VR-FALD*.

result VR-FALD* corrects client drift due to statistical heterogeneity, improving convergence.

Bayesian inference Federated learning Langevin dynamics Control variates Wasserstein distance

2022-10-31abs ↗pdf ↗

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Upstat.ML

Paper analyzes Scaffold algorithm for federated learning, proving linear speed-up with stochastic gradients.

problem Understanding the impact of stochastic gradients on the Scaffold algorithm's performance.

method Proved linear speed-up in the number of clients using a Markov chain analysis of global parameters and control variates.

result Scaffold achieves linear speed-up in the number of clients up to higher-order terms in the step size, but retains a higher-order bias.

federated learning Scaffold algorithm stochastic gradients Wasserstein distance Markov chain

2025-03-10abs ↗pdf ↗

A Federated Many-to-One Hopfield model for associative Neural Networkscond-mat.dis-nn

A federated model learns shared archetypes from heterogeneous clients in continual learning.

problem Federated learning struggles with client heterogeneity and streaming distribution shifts.

method Clients encode their data as low-rank Hebbian operators, which are sent to a central server for aggregation and factorization into global archetypes.

result Improved global archetype reconstruction and associative retrieval in heterogeneous clients, drift, and novelty settings.

federated learning associative memory heterogeneous clients continual learning low-rank operators

2026-03-20abs ↗pdf ↗

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learningstat.ML

SCAFFLSA reduces communication complexity for federated learning with heterogeneous clients.

problem Quantifying and reducing communication complexity in federated learning with heterogeneous clients.

method Proposes SCAFFLSA, a variant of FedLSA using control variates to correct for client drift.

result SCAFFLSA achieves logarithmic communication complexity for statistically heterogeneous agents, scaling with the inverse of the desired accuracy.

federated learning heterogeneous clients communication complexity control variates temporal difference learning

2024-02-06abs ↗pdf ↗

Faster Non-Convex Federated Learning via Global and Local Momentumstat.ML

FedGLOMO accelerates FL convergence for non-convex functions.

problem Efficiently solving non-convex optimization problems in federated learning with client heterogeneity.

method Combines global and local momentum updates to reduce variance and improve convergence rate.

result Achieves