:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Li, Yanghao, Liu, Changxin, Yi, Yuhao
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2603.15144
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Byzantine-Robust and Communication-Efficient Distributed Learning via Compressed Momentum Filtering
by: Liu, Changxin, et al.
Published: (2024)

Reconciling Communication Compression and Byzantine-Robustness in Distributed Learning
by: Gupta, Diksha, et al.
Published: (2025)

Global Momentum Compression for Sparse Communication in Distributed Learning
by: Shi, Chang-Wei, et al.
Published: (2019)

Variance-Adaptive Muon: Accelerating LLM Pretraining with NSR-Modulated and Variance-Scaled Momentum
by: Li, Jingru, et al.
Published: (2026)

Muon is Provably Faster with Momentum Variance Reduction
by: Qian, Xun, et al.
Published: (2025)

Byzantine-Robust Distributed Sparse Learning Revisited
by: Wang, Yuxuan, et al.
Published: (2026)

Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
by: Rammal, Ahmad, et al.
Published: (2023)

Nesterov-Accelerated Robust Federated Learning Over Byzantine Adversaries
by: Xu, Lihan, et al.
Published: (2025)

Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers
by: Yi, Yuhao, et al.
Published: (2023)

Delayed Momentum Aggregation: Communication-efficient Byzantine-robust Federated Learning with Partial Participation
by: Otsuka, Kaoru, et al.
Published: (2025)

Byzantine-Resilient Federated Learning via Distributed Optimization
by: Xia, Yufei, et al.
Published: (2025)

Coded Robust Aggregation for Distributed Learning under Byzantine Attacks
by: Li, Chengxi, et al.
Published: (2025)

Byzantine-Robust Federated Learning over Ring-All-Reduce Distributed Computing
by: Fang, Minghong, et al.
Published: (2025)

Global Convergence of Natural Policy Gradient with Hessian-aided Momentum Variance Reduction
by: Feng, Jie, et al.
Published: (2024)

Efficient Sign-Based Optimization: Accelerating Convergence via Variance Reduction
by: Jiang, Wei, et al.
Published: (2024)

Adaptive Optimization via Momentum on Variance-Normalized Gradients
by: Patitucci, Francisco, et al.
Published: (2026)

Drago: Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
by: Mehta, Ronak, et al.
Published: (2024)

Dynamic Byzantine-Robust Learning: Adapting to Switching Byzantine Workers
by: Dorfman, Ron, et al.
Published: (2024)

Robust Federated Learning via Byzantine Filtering over Encrypted Updates
by: Bendoukha, Adda Akram, et al.
Published: (2026)

Byzantine-Robust Decentralized Federated Learning
by: Fang, Minghong, et al.
Published: (2024)

Variance-Based Pruning for Accelerating and Compressing Trained Networks
by: Berisha, Uranik, et al.
Published: (2025)

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

BRFL: A Blockchain-based Byzantine-Robust Federated Learning Model
by: Li, Yang, et al.
Published: (2023)

Rennala MVR: Improved Time Complexity for Parallel Stochastic Optimization via Momentum-Based Variance Reduction
by: Tovmasyan, Zhirayr, et al.
Published: (2026)

Variance Reduction via Resampling and Experience Replay
by: Han, Jiale, et al.
Published: (2025)

Byzantine-Robust Distributed SGD: A Unified Analysis and Tight Error Bounds
by: Ruan, Boyuan, et al.
Published: (2026)

Non-Convex Optimization in Federated Learning via Variance Reduction and Adaptive Learning
by: Thakur, Dipanwita, et al.
Published: (2024)

Byzantine-Robust Federated Learning with Learnable Aggregation Weights
by: Parsa, Javad, et al.
Published: (2025)

BOBA: Byzantine-Robust Federated Learning with Label Skewness
by: Bao, Wenxuan, et al.
Published: (2022)

On the Reduction of Variance and Overestimation of Deep Q-Learning
by: Sabry, Mohammed, et al.
Published: (2019)

On Variance Reduction in Learning Mean Flows
by: Lu, Juanwu, et al.
Published: (2026)

Policy-based Primal-Dual Methods for Concave CMDP with Variance Reduction
by: Ying, Donghao, et al.
Published: (2022)

ImprovDML: Improved Trade-off in Private Byzantine-Resilient Distributed Machine Learning
by: Liu, Bing, et al.
Published: (2025)

SPAM: Stochastic Proximal Point Method with Momentum Variance Reduction for Non-convex Cross-Device Federated Learning
by: Karagulyan, Avetik, et al.
Published: (2024)

Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction
by: Byambadalai, Undral, et al.
Published: (2024)

Dynamic Meta-Layer Aggregation for Byzantine-Robust Federated Learning
by: Das, Reek, et al.
Published: (2026)

Strategies for Improving Communication Efficiency in Distributed and Federated Learning: Compression, Local Training, and Personalization
by: Yi, Kai
Published: (2025)

Optimal Complexity in Byzantine-Robust Distributed Stochastic Optimization with Data Heterogeneity
by: Shi, Qiankun, et al.
Published: (2025)

Byzantine Failures Harm the Generalization of Robust Distributed Learning Algorithms More Than Data Poisoning
by: Boudou, Thomas, et al.
Published: (2025)

The Robustness of Spiking Neural Networks in Federated Learning with Compression Against Non-omniscient Byzantine Attacks
by: Nguyen, Manh V., et al.
Published: (2025)