Cover Image

Refined Analysis of Federated Averaging and Federated Richardson-Romberg

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Mangold, Paul, Durmus, Alain, Dieuleveut, Aymeric, Samsonov, Sergey, Moulines, Eric
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Scaffold with Stochastic Gradients: New Analysis with Linear Speed-Up
by: Mangold, Paul, et al.
Published: (2025)

Nonasymptotic Analysis of Stochastic Gradient Descent with the Richardson-Romberg Extrapolation
by: Sheshukova, Marina, et al.
Published: (2024)

Gaussian Approximation and Multiplier Bootstrap for Federated Linear Stochastic Approximation
by: Levin, Ilya, et al.
Published: (2026)

SCAFFLSA: Taming Heterogeneity in Federated Linear Stochastic Approximation and TD Learning
by: Mangold, Paul, et al.
Published: (2024)

Compressed and distributed least-squares regression: convergence rates with applications to Federated Learning
by: Philippenko, Constantin, et al.
Published: (2023)

High-Order Error Bounds for Markovian LSA with Richardson-Romberg Extrapolation
by: Levin, Ilya, et al.
Published: (2025)

Federated Majorize-Minimization: Beyond Parameter Aggregation
by: Dieuleveut, Aymeric, et al.
Published: (2025)

Unified Breakdown Analysis for Byzantine Robust Gossip
by: Gaucher, Renaud, et al.
Published: (2024)

From Inexact Gradients to Byzantine Robustness: Acceleration and Optimization under Similarity
by: Gaucher, Renaud, et al.
Published: (2026)

A Tight Theory of Error Feedback Algorithms in Distributed Optimization
by: Thomsen, Daniel Berg, et al.
Published: (2026)

First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
by: Beznosikov, Aleksandr, et al.
Published: (2023)

Improved High-Probability Bounds for the Temporal Difference Learning Algorithm via Exponential Stability
by: Samsonov, Sergey, et al.
Published: (2023)

Tight analyses of first-order methods with error feedback
by: Thomsen, Daniel Berg, et al.
Published: (2025)

Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between Model-Parameter Staleness and Update Frequency
by: Alahyane, Abdelkrim, et al.
Published: (2025)

On the Rate of Gaussian Approximation for Linear Regression Problems
by: Khusainov, Marat, et al.
Published: (2025)

Statistical inference for Linear Stochastic Approximation with Markovian Noise
by: Samsonov, Sergey, et al.
Published: (2025)

Gaussian Approximation and Multiplier Bootstrap for Polyak-Ruppert Averaged Linear Stochastic Approximation with Applications to TD Learning
by: Samsonov, Sergey, et al.
Published: (2024)

Continuous-Time Analysis of Federated Averaging
by: Overman, Tom, et al.
Published: (2025)

Incentive-Aware Federated Averaging with Performance Guarantees under Strategic Participation
by: Maleki, Fateme, et al.
Published: (2026)

Tight Analysis of Decentralized SGD: A Markov Chain Perspective
by: Versini, Lucas, et al.
Published: (2026)

UVIP: Model-Free Approach to Evaluate Reinforcement Learning Algorithms
by: Belomestny, Denis, et al.
Published: (2021)

Inexact subgradient methods for semialgebraic functions
by: Bolte, Jérôme, et al.
Published: (2024)

PEPit: computer-assisted worst-case analyses of first-order optimization methods in Python
by: Goujaud, Baptiste, et al.
Published: (2022)

Provable non-accelerations of the heavy-ball method
by: Goujaud, Baptiste, et al.
Published: (2023)

Counter-examples in first-order optimization: a constructive approach
by: Goujaud, Baptiste, et al.
Published: (2023)

Open Problem: Two Riddles in Heavy-Ball Dynamics
by: Goujaud, Baptiste, et al.
Published: (2025)

Convergence Guarantees for Federated SARSA with Local Training and Heterogeneous Agents
by: Mangold, Paul, et al.
Published: (2025)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent
by: Hendrikx, Hadrien, et al.
Published: (2023)

Accelerated Methods with Complexity Separation Under Data Similarity for Federated Learning Problems
by: Bylinkin, Dmitry, et al.
Published: (2026)

High-Probability Bounds for SGD under the Polyak-Lojasiewicz Condition with Markovian Noise
by: Kar, Avik, et al.
Published: (2026)

Finite-Time Analysis of On-Policy Heterogeneous Federated Reinforcement Learning
by: Zhang, Chenyu, et al.
Published: (2024)

Stochastic Controlled Averaging for Federated Learning with Communication Compression
by: Huang, Xinmeng, et al.
Published: (2023)

In-depth Analysis of Low-rank Matrix Factorisation in a Federated Setting
by: Philippenko, Constantin, et al.
Published: (2024)

Federated Instrumental Variable Analysis via Federated Generalized Method of Moments
by: Geetika, et al.
Published: (2025)

Stochastic Approximation with Biased MCMC for Expectation Maximization
by: Gruffaz, Samuel, et al.
Published: (2024)

A Unified Analysis for Finite Weight Averaging
by: Wang, Peng, et al.
Published: (2024)

Locally Adaptive Federated Learning
by: Mukherjee, Sohom, et al.
Published: (2023)

Controlling Participation in Federated Learning with Feedback
by: Cummins, Michael, et al.
Published: (2024)

Federated ADMM from Bayesian Duality
by: Möllenhoff, Thomas, et al.
Published: (2025)

Federated Composite Saddle Point Optimization
by: Bai, Site, et al.
Published: (2023)