Cover Image

Residual-Evasive Attacks on ADMM in Distributed Optimization

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bruckmeier, Sabrina, Mo, Huadong, Qin, James
Format:	Preprint
Published:	2025
Subjects:	Cryptography and Security Distributed, Parallel, and Cluster Computing Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

FedADMM-InSa: An Inexact and Self-Adaptive ADMM for Federated Learning
by: Song, Yongcun, et al.
Published: (2024)

Smoothed Normalization for Efficient Distributed Private Optimization
by: Shulgin, Egor, et al.
Published: (2025)

The Privacy Power of Correlated Noise in Decentralized Learning
by: Allouah, Youssef, et al.
Published: (2024)

Seeking Consensus on Subspaces in Federated Principal Component Analysis
by: Wang, Lei, et al.
Published: (2020)

Find a Scapegoat: Poisoning Membership Inference Attack and Defense to Federated Learning
by: Mo, Wenjin, et al.
Published: (2025)

Robust Federated Learning Mitigates Client-side Training Data Distribution Inference Attacks
by: Xu, Yichang, et al.
Published: (2024)

Distributed Constraint-Coupled Optimization: Harnessing ADMM-consensus for robustness
by: Messilem, Mohamed Abdelmouamin, et al.
Published: (2025)

Poisoning Attacks and Defenses to Federated Unlearning
by: Wang, Wenbin, et al.
Published: (2025)

SecureSplit: Mitigating Backdoor Attacks in Split Learning
by: Dou, Zhihao, et al.
Published: (2026)

Detecting Backdoor Attacks in Federated Learning via Direction Alignment Inspection
by: Xu, Jiahao, et al.
Published: (2025)

On the Hardness of Decentralized Multi-Agent Policy Evaluation under Byzantine Attacks
by: Hairi, et al.
Published: (2024)

Defending Against Data Reconstruction Attacks in Federated Learning: An Information Theory Approach
by: Tan, Qi, et al.
Published: (2024)

SkyMask: Attack-agnostic Robust Federated Learning with Fine-grained Learnable Masks
by: Yan, Peishen, et al.
Published: (2023)

TimberStrike: Dataset Reconstruction Attack Revealing Privacy Leakage in Federated Tree-Based Systems
by: Di Gennaro, Marco, 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)

A Whole-Process Certifiably Robust Aggregation Method Against Backdoor Attacks in Federated Learning
by: Zhou, Anqi, et al.
Published: (2024)

Beyond Denial-of-Service: The Puppeteer's Attack for Fine-Grained Control in Ranking-Based Federated Learning
by: Chen, Zhihao, et al.
Published: (2026)

Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
by: Zhang, Hangtao, et al.
Published: (2023)

SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split Learning (Full Version)
by: Rieger, Phillip, et al.
Published: (2025)

Convergence of Sign-based Random Reshuffling Algorithms for Nonconvex Optimization
by: Qin, Zhen, et al.
Published: (2023)

DisAgg: Distributed Aggregators for Efficient Secure Aggregation in Federated Learning
by: Mehmood, Haaris, et al.
Published: (2026)

High Dimensional Distributed Gradient Descent with Arbitrary Number of Byzantine Attackers
by: Liu, Wenyu, et al.
Published: (2023)

Achieving Near-Optimal Convergence for Distributed Minimax Optimization with Adaptive Stepsizes
by: Huang, Yan, et al.
Published: (2024)

Accelerating Distributed Optimization: A Primal-Dual Perspective on Local Steps
by: Yang, Junchi, et al.
Published: (2024)

Lower Bounds and Accelerated Algorithms in Distributed Stochastic Optimization with Communication Compression
by: He, Yutong, et al.
Published: (2023)

Unbiased Compression Saves Communication in Distributed Optimization: When and How Much?
by: He, Yutong, et al.
Published: (2023)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
by: Cyffers, Edwige, et al.
Published: (2022)

Upcycling Noise for Federated Unlearning
by: Chen, Jianan, et al.
Published: (2024)

Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning
by: Yang, Hansi, et al.
Published: (2024)

Accelerated Methods with Compressed Communications for Distributed Optimization Problems under Data Similarity
by: Bylinkin, Dmitry, et al.
Published: (2024)

Rescaled Asynchronous SGD: Optimal Distributed Optimization under Data and System Heterogeneity
by: Mahran, Ammar, et al.
Published: (2026)

Enhancing Privacy in Decentralized Min-Max Optimization: A Differentially Private Approach
by: Quan, Yueyang, et al.
Published: (2025)

Byzantine-Resilient Zero-Order Optimization for Communication-Efficient Heterogeneous Federated Learning
by: Egger, Maximilian, et al.
Published: (2025)

Secure, Verifiable, and Scalable Multi-Client Data Sharing via Consensus-Based Privacy-Preserving Data Distribution
by: Panth, Prajwal, et al.
Published: (2026)

SENTINEL: Securing Indoor Localization against Adversarial Attacks with Capsule Neural Networks
by: Gufran, Danish, et al.
Published: (2024)

Proving the Limited Scalability of Centralized Distributed Optimization via a New Lower Bound Construction
by: Tyurin, Alexander
Published: (2025)

A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
by: Shi, Xinli, et al.
Published: (2025)

End-to-End Verifiable Decentralized Federated Learning
by: Lee, Chaehyeon, et al.
Published: (2024)

On Biased Compression for Distributed Learning
by: Beznosikov, Aleksandr, et al.
Published: (2020)

GeoShield: Byzantine Fault Detection and Recovery for Geo-Distributed Real-Time Cyber-Physical Systems
by: Cai, Yifan, et al.
Published: (2025)