The paper introduces Fed-ADR, a coordinated attack framework where a malicious orchestrator server directs heterogeneous adversarial clients to adapt their gradient updates in real time, thereby evading existing federated learning defenses and drastically reducing global model accuracy. It also presents a lightweight detection mechanism that estimates true client gradients from historical data to spot coordinated attacks, and an in-situ recovery method that restores model performance without restarting training. Experiments on MNIST, Fashion‑MNIST, and CIFAR‑10 show the attack can drop accuracy from over 90% to below 10%, while the defense can recover accuracy to above 90% within a few rounds at a computational cost at least 20× lower than retraining from scratch.
By Mohamed Shaaban, Ahmed Abdelnaby, Mohamed Elmahallawy
The paper introduces Robust Async-Fed-Q, an epoch‑based federated Q‑learning algorithm that combines variance‑reduced Bellman operator estimation with robust server aggregation. It provides high‑probability finite‑time guarantees showing that honest agents retain sample‑efficiency benefits even when some agents act adversarially, with the adversarial impact diminishing as honest data grows. The authors also present matching lower bounds, extend the framework to single‑trajectory sampling and heterogeneous coverage, and improve communication complexity for asynchronous federated Q‑learning.
By Sreejeet Maity, Aritra Mitra
The paper introduces Adversarial Importance Sampling (Advis), a technique that leverages importance sampling over standard training trajectories to estimate and optimize worst‑case returns without extra environment interactions or auxiliary networks, thereby capturing long‑term robustness. It also presents advrl, a modular PyTorch library that consolidates existing robustness methods and adversarial attacks into single‑file implementations for easier prototyping and reproducible evaluation. Finally, the authors highlight that optimal adversarial hyperparameters do not transfer across agents, prompting evaluation against a broader set of attackers (6–14× more configurations) and demonstrate the effectiveness of their approach on continuous control tasks.
By Amine Andam, Jamal Bentahar, Mustapha Hedabou
arXiv:2607. 06643v1 Announce Type: cross Abstract: Backdoor attacks severely threaten large-scale AI models.
By Issam Seddik, Sami Souihi, Mohamed Tamaazousti, Sara Tucci Piergiovanni
arXiv:2608. 06637v1 Announce Type: cross Abstract: Robust aggregation methods are widely used in federated learning to mitigate the impact of adversarial client behavior.
By Srinivasan Subramanian, Md. Abdullah Al Hafiz Khan, Kazi Aminul Islam
The paper examines inference‑time observation attacks on Safe Multi‑Agent Reinforcement Learning (SMARL), where an adversary perturbs observations of some agents to alter their actions. It introduces the MinMax Attack framework, which jointly selects adversarial target actions for compromised agents while considering the cooperative resistance of benign agents. The authors derive a bound on the induced safety cost and show through experiments that MinMax Attack causes higher safety costs than existing baselines while maintaining task reward, thus improving attack effectiveness and stealthiness.
By Taozheng Zhu, Shixiong Jiang, Fanxin Kong
arXiv:2608.21137v1 Announce Type: new
Abstract: Decentralized Federated Learning (DFL) promises trust-free collaborative learning by replacing the centralized parameter server with peer-to-peer model...
By Mouhamed Amine Bouchiha, Gregory Blanc, Yufei Han
arXiv:2602.16543v2 Announce Type: replace
Abstract: Safe reinforcement learning (Safe RL) learns robotic controllers that optimize task rewards under safety constraints, yet observation perturbations...
By Jialiang Fan, Shixiong Jiang, Mengyu Liu, Fanxin Kong
The paper introduces Ask the Expert, a training-time framework that leverages large language models (LLMs) to guide reinforcement learning for autonomous cyber defense. It summarizes complex cyber‑defense states, intermittently queries an LLM for host‑level defensive recommendations via a constrained action interface, and then uses those recommendations to shape rewards for PPO training. After training, the LLM is discarded, leaving a pure RL policy that improves sample efficiency and outperforms potential‑based reward shaping baselines on TTCP CAGE CC1 and CC2 scenarios.
By Fernando Martinez, Abhishek Satyam, Tao Li, Junaid Farooq, Ying Wang, Juntao Chen
arXiv:2607. 08137v1 Announce Type: cross Abstract: Federated reinforcement learning (FRL) is crucial for enabling collaborative learning across multiple agents without sharing raw data, thereby enhancing privacy and scalability in the decision-making process within dynamic vehicular environments.
By Zifan Zhang, Minghong Fang, Dianwei Chen, Zhuqing Liu, Prashant Khanduri, Xianfeng Yang, Anupam Das, Yuchen Liu
arXiv:2606. 12896v1 Announce Type: cross Abstract: While real-world applications of reinforcement learning (RL) are becoming increasingly popular, the security of RL systems deserve more attention and exploration.
By Junfeng Guo Heng Huang
arXiv:2509. 20008v2 Announce Type: replace Abstract: Penetration testing, the simulation of cyberattacks to identify security vulnerabilities, presents a sequential decision-making problem well-suited for reinforcement learning (RL) automation.
By Raphael Simon, Pieter Libin, Wim Mees