Cover Image

Distributed Quasi-Newton Method for Fair and Fast Federated Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Hamidi, Shayan Mohajer, Ye, Linfeng
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Thundernna: a white box adversarial attack
by: Ye, Linfeng, et al.
Published: (2021)

AdaFed: Fair Federated Learning via Adaptive Common Descent Direction
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Rate-Constrained Quantization for Communication-Efficient Federated Learning
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Towards Undistillable Models by Minimizing Conditional Mutual Information
by: Ye, Linfeng, et al.
Published: (2025)

Over-the-Air Fair Federated Learning via Multi-Objective Optimization
by: Hamidi, Shayan Mohajer, et al.
Published: (2025)

Coded Deep Learning: Framework and Algorithm
by: Yang, En-hui, et al.
Published: (2025)

Bayes Conditional Distribution Estimation for Knowledge Distillation Based on Conditional Mutual Information
by: Ye, Linfeng, et al.
Published: (2024)

Conditional Mutual Information Based Diffusion Posterior Sampling for Solving Inverse Problems
by: Hamidi, Shayan Mohajer, et al.
Published: (2025)

How to Train the Teacher Model for Effective Knowledge Distillation
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

GP-FL: Model-Based Hessian Estimation for Second-Order Over-the-Air Federated Learning
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Regime-aware financial volatility forecasting via in-context learning
by: Asaad, Saba, et al.
Published: (2026)

Coupled Data and Measurement Space Dynamics for Enhanced Diffusion Posterior Sampling
by: Hamidi, Shayan Mohajer, et al.
Published: (2025)

Information-Guided Diffusion Sampling for Dataset Distillation
by: Ye, Linfeng, et al.
Published: (2025)

Continual Learning With Quasi-Newton Methods
by: Eeckt, Steven Vander, et al.
Published: (2025)

Sharpened Lazy Incremental Quasi-Newton Method
by: Lahoti, Aakash, et al.
Published: (2023)

Online Learning Guided Quasi-Newton Methods with Global Non-Asymptotic Convergence
by: Jiang, Ruichen, et al.
Published: (2024)

Enhancing Diffusion Models for Inverse Problems with Covariance-Aware Posterior Sampling
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Simple Stepsize for Quasi-Newton Methods with Global Convergence Guarantees
by: Agafonov, Artem, et al.
Published: (2025)

Self-Scaled Broyden Family of Quasi-Newton Methods in JAX
by: Bioli, Ivan, et al.
Published: (2026)

Incremental Quasi-Newton Methods with Faster Superlinear Convergence Rates
by: Liu, Zhuanghua, et al.
Published: (2024)

Entropy-driven Fair and Effective Federated Learning
by: Wang, Lin, et al.
Published: (2023)

Learning Heterogeneous Performance-Fairness Trade-offs in Federated Learning
by: Ye, Rongguang, et al.
Published: (2025)

A Proximal Modified Quasi-Newton Method for Nonsmooth Regularized Optimization
by: Diouane, Youssef, et al.
Published: (2024)

Distribution-Free Fair Federated Learning with Small Samples
by: Yin, Qichuan, et al.
Published: (2024)

FedNS: A Fast Sketching Newton-Type Algorithm for Federated Learning
by: Li, Jian, et al.
Published: (2024)

A Learn-to-Optimize Approach for Coordinate-Wise Step Sizes for Quasi-Newton Methods
by: Lin, Wei, et al.
Published: (2024)

Fairness Regularization in Federated Learning
by: Kharaghani, Zahra, et al.
Published: (2025)

Communication-Efficient Federated Learning With Data and Client Heterogeneity
by: Zakerinia, Hossein, et al.
Published: (2022)

Improved Complexity for Smooth Nonconvex Optimization: A Two-Level Online Learning Approach with Quasi-Newton Methods
by: Jiang, Ruichen, et al.
Published: (2024)

Preconditioned Norms: A Unified Framework for Steepest Descent, Quasi-Newton and Adaptive Methods
by: Veprikov, Andrey, et al.
Published: (2025)

A Stochastic Quasi-Newton Method for Non-convex Optimization with Non-uniform Smoothness
by: Sun, Zhenyu, et al.
Published: (2024)

Symmetric Rank-One Quasi-Newton Methods for Deep Learning Using Cubic Regularization
by: Ranganath, Aditya, et al.
Published: (2025)

On the Necessity of Metalearning: Learning Suitable Parameterizations for Learning Processes
by: Hamidi, Massinissa, et al.
Published: (2023)

Fairness in Federated Learning: Fairness for Whom?
by: Taik, Afaf, et al.
Published: (2025)

Fair Federated Learning under Domain Skew with Local Consistency and Domain Diversity
by: Chen, Yuhang, et al.
Published: (2024)

Fast Gauss-Newton for Multiclass Cross-Entropy
by: Korbit, Mikalai, et al.
Published: (2026)

Federated Learning with Relative Fairness
by: Nakakita, Shogo, et al.
Published: (2024)

WassFFed: Wasserstein Fair Federated Learning
by: Han, Zhongxuan, et al.
Published: (2024)