The Hidden Vulnerability of Distributed Learning in Byzantium (1802.07927v2)

Abstract: While machine learning is going through an era of celebrated success, concerns have been raised about the vulnerability of its backbone: stochastic gradient descent (SGD). Recent approaches have been proposed to ensure the robustness of distributed SGD against adversarial (Byzantine) workers sending poisoned gradients during the training phase. Some of these approaches have been proven Byzantine-resilient: they ensure the convergence of SGD despite the presence of a minority of adversarial workers. We show in this paper that convergence is not enough. In high dimension $d \gg 1$, an adver-sary can build on the loss function's non-convexity to make SGD converge to ineffective models. More precisely, we bring to light that existing Byzantine-resilient schemes leave a margin of poisoning of $\Omega\left(f(d)\right)$, where $f(d)$ increases at least like $\sqrt{d~}$. Based on this leeway, we build a simple attack, and experimentally show its strong to utmost effectivity on CIFAR-10 and MNIST. We introduce Bulyan, and prove it significantly reduces the attackers leeway to a narrow $O( \frac{1}{\sqrt{d~}})$ bound. We empirically show that Bulyan does not suffer the fragility of existing aggregation rules and, at a reasonable cost in terms of required batch size, achieves convergence as if only non-Byzantine gradients had been used to update the model.

• The paper introduces virtuous safety by integrating safe interruptibility and adversarial robustness to ensure RL agents remain secure in uncertain conditions.
• The authors adapt established algorithms like Q-Learning and Sarsa, achieving improved resilience and recovery through empirical validation.
• The research demonstrates practical implications for deploying reliable, intervention-ready RL systems and sets the stage for future safety innovations.

Virtuously Safe Reinforcement Learning

The paper "Virtuously Safe Reinforcement Learning" explores the critical concept of enhancing safety within reinforcement learning (RL) frameworks. The authors, Henrik Aslund, El Mahdi El Mhamdi, Rachid Guerraoui, and Alexandre Maurer, address the challenge of developing RL systems that can operate reliably in dynamic and uncertain environments, particularly focusing on mechanisms that allow these systems to handle interruptions and adversarial conditions effectively.

Key Concepts and Methodology

The paper introduces the notion of "virtuous safety" in RL, which involves the integration of Safe Interruptibility and adversarial robustness into existing RL algorithms. These elements promote the creation of agents that can function safely even when their operational environment or underlying assumptions are compromised.

The research primarily investigates the adaptation of well-established RL algorithms, such as Q-Learning and Sarsa, with these safety features. Safe Interruptibility ensures that an RL agent can be safely stopped and restarted without impacting its learning process negatively. This concept is crucial in scenarios where human intervention may be necessary to prevent undesirable actions by the agent.

Furthermore, the authors delve into adversarial machine learning, a context wherein agents must contend with potentially deceptive inputs designed to lead them to erroneous decisions. The paper highlights methodologies to reinforce perception mechanisms within RL agents, thereby enhancing their resistance to such adversarial perturbations.

Numerical Results

The authors substantiate their propositions with empirical results, demonstrating that their enhanced RL algorithms outperform standard counterparts under various failure scenarios. These experiments emphasize that agents equipped with Safe Interruptibility and adversarial robustness maintain higher levels of safety and reliability. The results indicate a significant improvement in the agents' ability to recover and adapt following external disruptions.

Implications and Future Directions

The implications of this research extend both practically and theoretically. On a practical level, the integration of safe interruptibility and adversarial resistance into RL systems is vital for deploying these systems in real-world applications, where unpredictable conditions and the necessity for human oversight are common.

From a theoretical perspective, this work encourages further exploration into hybrid approaches that combine multiple safety features within RL frameworks. The notion of "virtuously safe" RL offers a promising avenue for future research, guiding efforts towards constructing agents that not only seek optimal policies but do so while adhering to stringent safety protocols.

Looking ahead, the research community may explore enhancing these safety mechanisms by utilizing advanced deep RL architectures or integrating additional safety layers, such as robust state estimation and action verification processes. The challenge remains to balance efficiency with safety, ensuring that the pursuit of optimal solutions does not compromise the agent's integrity or reliability.

In conclusion, the exploration of virtuous safety in reinforcement learning significantly contributes to the ongoing discourse on safe AI deployment. By advancing understanding and implementation of these safety features, the research paves the way for more resilient and trustworthy AI systems.