Cover Image

Optimization Trade-offs in Asynchronous Federated Learning: A Stochastic Networks Approach

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Alahyane, Abdelkrim, Comte, Céline, Jonckheere, Matthieu
Format:	Preprint
Published:	2026
Subjects:	Machine Learning Performance Optimization and Control Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Score-Aware Policy-Gradient and Performance Guarantees using Local Lyapunov Stability
by: Comte, Céline, et al.
Published: (2023)

Admission Control of Quasi-Reversible Queueing Systems: Optimization and Reinforcement Learning
by: Comte, Céline, et al.
Published: (2025)

The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
by: Scully, Ziv, et al.
Published: (2025)

Adversarial Network Optimization under Bandit Feedback: Maximizing Utility in Non-Stationary Multi-Hop Networks
by: Dai, Yan, et al.
Published: (2024)

Diffusion-Based Stochastic Operator Networks for Uncertainty Quantification in Stochastic Partial Differential Equations
by: Huynh, Phuoc-Toan, et al.
Published: (2026)

Accelerating Distributed Stochastic Optimization via Self-Repellent Random Walks
by: Hu, Jie, et al.
Published: (2024)

Graph-Based Product Form
by: Comte, Céline, et al.
Published: (2025)

A Generalization Result for Convergence in Learning-to-Optimize
by: Sucker, Michael, et al.
Published: (2024)

Efficient Solving of Large Single Input Superstate Decomposable Markovian Decision Process
by: Mahjoub, Youssef Ait El, et al.
Published: (2025)

Flatness-Aware Stochastic Gradient Langevin Dynamics
by: Bruno, Stefano, et al.
Published: (2025)

When Machine Learning Meets Importance Sampling: A More Efficient Rare Event Estimation Approach
by: Zhao, Ruoning, et al.
Published: (2025)

Wasserstein Formulation of Reinforcement Learning. An Optimal Transport Perspective on Policy Optimization
by: Dus, Mathias
Published: (2026)

Online Learning and Optimization for Queues with Unknown Demand Curve and Service Distribution
by: Chen, Xinyun, et al.
Published: (2023)

Stochastic Inverse Problem: stability, regularization and Wasserstein gradient flow
by: Li, Qin, et al.
Published: (2024)

Concentration of General Stochastic Approximation Under Heavy-Tailed Markovian Noise
by: Agrawal, Shubhada, et al.
Published: (2026)

Controlling the Flow: Stability and Convergence for Stochastic Gradient Descent with Decaying Regularization
by: Kassing, Sebastian, et al.
Published: (2025)

Decoupled Functional Central Limit Theorems for Two-Time-Scale Stochastic Approximation
by: Han, Yuze, et al.
Published: (2024)

Algorithmic Stability of Stochastic Gradient Descent with Momentum under Heavy-Tailed Noise
by: Dang, Thanh, et al.
Published: (2025)

Queues with resetting: a perspective
by: Roy, Reshmi, et al.
Published: (2024)

A Distributional View of High Dimensional Optimization
by: Benning, Felix
Published: (2025)

Control of parallel non-observable queues: asymptotic equivalence and optimality of periodic policies
by: Anselmi, Jonatha, et al.
Published: (2014)

Ito Diffusion Approximation of Universal Ito Chains for Sampling, Optimization and Boosting
by: Ustimenko, Aleksei, et al.
Published: (2023)

Data-driven Multistage Distributionally Robust Linear Optimization with Nested Distance
by: Gao, Rui, et al.
Published: (2024)

Improved Approximation Algorithms for Orthogonally Constrained Problems Using Semidefinite Optimization
by: Cory-Wright, Ryan, et al.
Published: (2025)

Convergence of SGD for Training Neural Networks with Sliced Wasserstein Losses
by: Tanguy, Eloi
Published: (2023)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
by: Sato, Kanji, et al.
Published: (2022)

Enhanced Innovized Repair Operator for Evolutionary Multi- and Many-objective Optimization
by: Mittal, Sukrit, et al.
Published: (2020)

The Sample-Communication Complexity Trade-off in Federated Q-Learning
by: Salgia, Sudeep, et al.
Published: (2024)

Trade-off in Estimating the Number of Byzantine Clients in Federated Learning
by: Chen, Ziyi, et al.
Published: (2025)

Asynchronous Load Balancing and Auto-scaling: Mean-Field Limit and Optimal Design
by: Anselmi, Jonatha
Published: (2022)

Value Mirror Descent for Reinforcement Learning
by: Jia, Zhichao, et al.
Published: (2026)

Reinforcement Learning with Random Time Horizons
by: Borrell, Enric Ribera, et al.
Published: (2025)

Learning-Based Pricing and Matching for Two-Sided Queues
by: Yang, Zixian, et al.
Published: (2024)

Stochastic Optimal Control Matching
by: Domingo-Enrich, Carles, et al.
Published: (2023)

Particle Filter Optimization: A Bayesian Approach for Global Stochastic Optimization
by: Eslami, Mostafa, et al.
Published: (2024)

Convergence of Actor-Critic Learning for Mean Field Games and Mean Field Control in Continuous Spaces
by: Fouque, Jean-Pierre, et al.
Published: (2025)

Decision-Epoch Matters: Unveiling its Impact on the Stability of Scheduling with Randomly Varying Connectivity
by: Soprano-Loto, Nahuel, et al.
Published: (2024)

Rates of Convergence in the Central Limit Theorem for Markov Chains, with an Application to TD Learning
by: Srikant, R.
Published: (2024)

A stochastic gradient descent algorithm with random search directions
by: Gbaguidi, Eméric
Published: (2025)