Byzantine Attacks

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Period ending 2026-09-21

2 new papers

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Period ending 2026-09-14

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Period ending 2026-09-07

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40 papers

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Fully Byzantine-Resilient Multi-Agent Reinforcement Learning
Sep 22, 2026Haejoon Lee, Dimitra PanagouMulti-Agent Reinforcement Learning Byzantine Attacks
SWB-DM: A Calibrated Sliced-Wasserstein-Barycenter Aggregator with Delayed-Momentum Caching for Byzantine-Robust Federated Learning under Partial Participation
Sep 14, 2026Saranraj S, Saranya M S, Alex David S +1Federated Learning Byzantine Attacks
End-to-End Verifiable and Robust Federated Learning
Sep 14, 2026Doryan Lesaignoux, Enrique Mármol Campos, Gabriele Spini +2Federated Learning Byzantine Attacks
Robust Decentralized Personalized Federated Learning via Prediction-Constrained Neighborhood Collaboration
Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Federated Learning Byzantine Attacks
Robust Decentralized Federated Distillation via Multi-Modality Knowledge Collaboration
Sep 7, 2026Xiao Ma, Hong Shen, Hui Tian +2Byzantine Attacks Gradient Concentration
Privacy, Robustness, and Fairness Trade-offs in Federated Intrusion Detection: Geometric Indistinguishability at the Aggregation Interface
Sep 3, 2026Adrita Rahman Tory, ABM Shawkat Ali, Md Abu Layek +1Federated Learning Intrusion Detection
Differentially private federated learning with Byzantine-robust aggregation: A cross-domain framework for secure model training in banking and healthcare systems
Sep 2, 2026Srikumar NayakFederated Learning Byzantine Attacks
Optimizing Byzantine Node Placement in Decentralized Federated Learning
Sep 1, 2026Edoardo Gabrielli, Gabriele TolomeiByzantine Attacks Federated Learning
Distributed Team Orchestration via Supervisor Networks: Convergence, Optimality, and Resilience
Aug 10, 2026Juntian Zhu, Guanpu Chen, Tongtian Zhu +3Byzantine Attacks Imperfect-Information Games
Robust Reputation-Driven Crowdsourced Federated Learning
Aug 9, 2026Mouhamed Amine Bouchiha, Gregory BlancFederated Learning Model-Agnostic Defense
Bypassing Krum: Selection-Aware Backdoor Attacks in Federated Learning
Aug 6, 2026Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul IslamFederated Learning Byzantine Attacks
Theoretical Foundations of Communication-Efficient, Robust, and Practical Distributed and Federated Optimization
Aug 6, 2026Grigory MalinovskyFederated Learning Distributed Optimization
Online Security Learning in Cooperative Multi-Agent Systems under Hidden Byzantine Attacks
Aug 6, 2026Ximing Sun, Yue WangByzantine Attacks Multi-Agent Systems
Fairis: Fairness-Aware Aggregation with Provable Influence Containment against Fairness Poisoning Attacks in Collaborative Machine Learning
Aug 6, 2026Devharsh Trivedi, Nesrine Kaaniche, Nikos Triandopoulos +2Fairness-Aware Learning Byzantine Attacks
FL-OA: A Byzantine-Robust Federated Learning Framework with Outsourced Auditing for Intelligent Devices
Aug 2, 2026Hongliang Zhang, Zhongyuan Yu, Fenghua Xu +3Federated Learning Byzantine Attacks
Enhanced Byzantine-Robust Federated Learning Via Truncated-Quadratic Loss for Heterogeneous Data
Jul 13, 2026Zhi-Yong Wang, Hao Nan Sheng, Werner Stefan +3Federated Learning Byzantine Attacks
Byzantine Accountability Without Consensus: Strong Eventual Consistency for Non-Associative, Stochastic, Robust Aggregation
Jul 11, 2026Ryan GillespieByzantine Attacks Consensus
PRoVeFL: Private Robust and Verifiable Aggregation in Federated Learning
Jul 7, 2026Harsh Kasyap, Anil Kumar Pradhan, Ugur Ilker Atmaca +2Federated Learning Byzantine Attacks
Unveiling the Non-Monotonic Effect of Privacy on Generalization under Byzantine Robustness
Jul 1, 2026Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Differential Privacy Generalization Bounds
RoPoLL: Robust Panel of LLM Judges
Jun 29, 2026Anish Acharya, Kris W Pan, Brian VerkhovskyLlm-As-A-Judge Large Language Model Judges
FoggyTrust: Robust Federated Learning with Hierarchical Trust Networks
Jun 26, 2026Emmanuel Rassou, Tomas GonzalezFederated Learning Byzantine Attacks
Giskard : Byzantine Robust and Confidential Aggregation for Large-Scale Decentralized Learning
Jun 17, 2026Ousmane Touat, César Sabater, Mohamed Maouche +1Byzantine Attacks Homomorphic Encryption
Byzantine Cheap Talk: Adversarial Resilience and Topology Effects in LLM Coordination Games
Jun 5, 2026Aya El Mir, Martin Takáč, Salem LahlouMulti-Agent Large Language Model Byzantine Attacks
Hierarchical Certified Semantic Commitment for Byzantine-Resilient LLM-Agent Collaboration
Jun 5, 2026Haoran Xu, Lei Zhang, Iadh Ounis +1Byzantine Attacks Ablation
Automated Byzantine-Resilient Clustered Decentralized Federated Learning for Battery Intelligence in Connected EVs
May 20, 2026Mouhamed Amine Bouchiha, Abdelaziz Amara Korba, Yacine Ghamri-DoudaneElectric Vehicles Federated Learning
Resilient Byzantine Agreement with Predictions
May 19, 2026Julien Dallot, Darya Melnyk, Tijana Milentijevic +2Byzantine Attacks Early Failure Prediction
Practical Validity Conditions for Byzantine-Tolerant Federated Learning
May 15, 2026Mélanie Cambus, Darya Melnyk, Tijana Milentijević +1Byzantine Attacks Federated Learning
Byzantine-Resilient Federated Learning via QUBO-Based Client Selection on Quantum Annealers
May 14, 2026Andras Ferenczi, Sutapa Samanta, Dagen Wang +1Federated Learning Byzantine Attacks
Byzantine-Robust Distributed Sparse Learning Revisited
May 13, 2026Yuxuan Wang, Lixin Zhang, Kangqiang LiByzantine Attacks Estimators
Attacks and Mitigations for Distributed Governance of Agentic AI under Byzantine Adversaries
May 12, 2026Matthew D. Laws, Alina Oprea, Cristina Nita-RotaruGovernance Layer Byzantine Attacks
Partial Model Sharing Improves Byzantine Resilience in Federated Conformal Prediction
May 12, 2026Ehsan Lari, Reza Arablouei, Stefan WernerByzantine Attacks Conformal Prediction
Robust Multi-Agent LLMs under Byzantine Faults
May 9, 2026Haejoon Lee, Vincent-Daniel Yun, Dimitra Panagou +1Multi-Agent Large Language Model Byzantine Attacks
AdaBFL: Multi-Layer Defensive Adaptive Aggregation for Bzantine-Robust Federated Learning
Apr 30, 2026Zehui Tang, Yuchen Liu, Feihu HuangFederated Learning Byzantine Attacks
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
Apr 16, 2026He Yang, Dongyi Lv, Wei Xi +3Federated Learning Byzantine Attacks
Decentralized Orchestration Architecture for Fluid Computing: A Secure Distributed AI Use Case
Mar 12, 2026Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +2Distributed Optimization Orchestration
Communication-Efficient Byzantine-Robust Federated Conformal Prediction via Partial Sharing
Feb 20, 2026Ehsan Lari, Reza Arablouei, Stefan WernerFederated Learning Conformal Prediction
A Nesterov-Accelerated Byzantine-Robust Federated Learning
Nov 4, 2025Lihan Xu, Xiaoyi Fan, Gang Wang +3Byzantine Attacks Federated Learning
Tight Stability Bounds for Robust Distributed Learning: Byzantine Failures Hurt Generalization More than Data Poisoning
Jun 22, 2025Thomas Boudou, Batiste Le Bars, Nirupam Gupta +1Distributional Learning Generalization Bounds
Byzantine-Robust Aggregation for Securing Decentralized Federated Learning
Sep 26, 2024Diego Cajaraville-Aboy, Ana Fernández-Vilas, Rebeca P. Díaz-Redondo +1Federated Learning Byzantine Attacks
Byzantine-Robust Federated Fire Detection with a Rotating Coordinator
Date pendingGeorgia Argyrou, Aymen Bahroun, Hedi Fendri +1Federated Learning Byzantine Attacks

Sep 22, 2026 · cs.LGJ/K move · Enter open · S save

Fully Byzantine-Resilient Multi-Agent Reinforcement Learning

Haejoon Lee, Dimitra Panagou

We study distributed Byzantine-resilient actor-critic multi-agent reinforcement learning (AC-MARL), where agents collectively learn policies through local interactions. Existing methods guarantee convergence of the agents' parameters only to a neighborhood of the attack-free limit points, resulting in degraded performance. We propose Fully Resilient AC-MARL (FRAC-MARL), a decentralized method in which each agent leverages redundancy in two-hop messages to identify reliable messages. Under linear parameterizations of the value and team-reward functions and Byzantine edge attacks, where adversarial behavior is confined to the communication layer, we prove that agents' parameters converge almost surely to the same limit points as in the attack-free case over time-varying communication graphs. We introduce a novel topological condition for the convergence of our method, present a systematic method to construct such networks, and prove that this condition can be verified in polynomial time. Finally, we demonstrate our method on cooperative multi-robot formation control tasks.

Multi-Agent Reinforcement Learning Byzantine Attacks Multi-Robot Systems

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