Decentralized Bandits: Surviving Bad Actors and Faulty Data

by

Author: Denis Avetisyan

A new algorithm, DeMABAR, offers robust performance in multi-agent learning scenarios where agents face both malicious attacks and unreliable information.

Decentralized algorithms—including DeMABAR, DRAA, resilient UCB, MA-BARBAT, IND-BARBAR, and IND-FTRL—demonstrate robustness against adversarial corruption when implemented within a centralized CMA2B framework.
Decentralized algorithms—including DeMABAR, DRAA, resilient UCB, MA-BARBAT, IND-BARBAR, and IND-FTRL—demonstrate robustness against adversarial corruption when implemented within a centralized CMA2B framework.

This paper introduces DeMABAR, a decentralized multi-armed bandit algorithm achieving improved regret minimization under adversarial corruption and Byzantine fault conditions.

Decentralized multi-agent systems offer compelling solutions for complex decision-making, yet remain vulnerable to malicious interference and unreliable agents. This paper, ‘Robust Decentralized Multi-armed Bandits: From Corruption-Resilience to Byzantine-Resilience’, addresses this critical gap by introducing DeMABAR, a novel algorithm designed for robust learning in environments plagued by both corrupted reward observations and strategically deceptive agents. Our analysis demonstrates that DeMABAR effectively minimizes regret even under significant adversarial influence, extending to full resilience in the challenging Byzantine setting where a fraction of agents may act arbitrarily. Can these techniques pave the way for truly reliable and scalable decentralized learning systems in real-world applications?

The Fragility of Collective Intelligence

As complex systems proliferate – from autonomous traffic networks and smart grids to robotic swarms and financial markets – the need for multi-agent reinforcement learning (MARL) becomes paramount. However, standard MARL algorithms, designed under the assumption of cooperative and well-behaved agents, exhibit surprising fragility when confronted with malicious or simply faulty actors. A single compromised agent can introduce deceptive signals, skew learning processes, and ultimately degrade the overall system performance, potentially causing cascading failures or even actively harmful outcomes. This vulnerability stems from the algorithms’ reliance on shared experiences and the difficulty in distinguishing between legitimate strategic behavior and intentional disruption, highlighting a critical gap between theoretical promise and practical deployment in real-world scenarios where adversarial or unreliable agents are increasingly inevitable.

While bandit algorithms offer a streamlined approach to learning optimal actions in relatively static environments, their efficacy diminishes considerably when applied to the complexities of multi-agent systems. These algorithms, designed to balance exploration and exploitation with a limited action space, encounter significant challenges when scaling to scenarios with numerous interacting agents and constantly shifting dynamics. The core issue lies in the assumption of a stationary environment; in multi-agent settings, the ‘reward landscape’ is continually reshaped by the learning and actions of other agents, rendering previously optimal strategies suboptimal. Furthermore, the computational cost of evaluating each action across all agents and potential future states quickly becomes prohibitive, exceeding the capabilities of traditional bandit methods and necessitating more sophisticated approaches to handle the scale and non-stationarity inherent in these complex systems.

The efficacy of multi-agent systems hinges on the trustworthiness of each component, yet the introduction of adversarial or Byzantine agents—those intentionally malicious or simply faulty—can drastically undermine overall performance. These compromised agents disrupt collaborative learning by providing inaccurate information or taking actions detrimental to the collective goal, leading to cascading failures and diminished rewards for all participants. Consequently, standard multi-agent reinforcement learning algorithms, designed under the assumption of cooperative rationality, exhibit significant vulnerability. Research now focuses on developing robust solutions – techniques capable of mitigating the impact of these corrupted agents through redundancy, anomaly detection, or incentive mechanisms – to ensure reliable operation even in the face of untrustworthy participants and maintain system-wide stability.

DeMABAR consistently outperforms resilient decentralized algorithms like UCB, IND-BARBAR, and IND-FTRL when facing adversarial corruption in the DeCMA2B environment.
DeMABAR consistently outperforms resilient decentralized algorithms like UCB, IND-BARBAR, and IND-FTRL when facing adversarial corruption in the DeCMA2B environment.

Decentralized Resilience: A Foundation for Robustness

Decentralized Robust Algorithms, such as DRAA and Decentralized Robust UCB, enhance system resilience by integrating robustness constraints directly into the algorithm’s learning phase. Traditional algorithms often assume a benign environment, but these approaches explicitly account for potential adversarial actions or compromised agents during the learning process. This is achieved by modifying the reward structure or objective function to penalize actions that are vulnerable to manipulation or failure under adverse conditions. Consequently, the learned policy prioritizes solutions that maintain performance even when a subset of agents exhibit malicious behavior or experience faults, offering a proactive approach to system stability compared to reactive fault tolerance mechanisms.

Decentralized algorithms, such as DRAA and Decentralized Robust UCB, distribute decision-making across multiple agents operating without a central coordinator. Each agent independently observes a local state and selects an action based on its own policy, informed by its individual experience and potentially limited communication with other agents. This architecture contrasts with centralized algorithms where a single entity processes all information and dictates actions. Collective optimal performance is achieved not through direct control, but through the convergence of individual, locally optimal decisions, often leveraging mechanisms for averaging or consensus to mitigate the impact of individual failures or biased observations. The robustness of these systems stems from the redundancy inherent in the distributed approach; the failure of a single agent does not necessarily impede the overall system’s ability to achieve its goals.

The performance of Decentralized Robust Algorithms, such as DRAA and Decentralized Robust UCB, is significantly impacted by the exploration rate, which is governed by the parameter $\lambda$. This parameter dictates the balance between exploring new actions and exploiting currently known optimal actions. In compromised environments, where some agents may report inaccurate information or be subject to adversarial attacks, a carefully tuned $\lambda$ is crucial. A higher $\lambda$ encourages greater exploration, increasing the likelihood of identifying and mitigating the impact of compromised agents, but potentially slowing down convergence. Conversely, a lower $\lambda$ prioritizes exploitation, which can lead to faster convergence in benign conditions but increases vulnerability to manipulation or incorrect information propagated by compromised agents. The optimal value of $\lambda$ is therefore context-dependent and requires careful consideration of the expected level of compromise and the algorithm’s sensitivity to inaccurate data.

DeMABAR-F outperforms resilient decentralized algorithms like UCB, IND-BARBAR, and IND-FTRL in the DeCMA2B environment when Byzantine agents (as shown in Figure 2) are present.
DeMABAR-F outperforms resilient decentralized algorithms like UCB, IND-BARBAR, and IND-FTRL in the DeCMA2B environment when Byzantine agents (as shown in Figure 2) are present.

Empirical Validation: Resilience in Action

Evaluations conducted within the Adversarial Corruption Setting confirm the robustness of several multi-agent reinforcement learning algorithms when subjected to malicious attacks. Specifically, IND-BARBAR, IND-FTRL, and MA-BARBAT consistently demonstrated maintained performance levels despite the presence of adversarial agents designed to disrupt learning. These experiments involved introducing corrupted agents that intentionally provide misleading information or take actions detrimental to overall system performance. The algorithms were then assessed based on metrics such as cumulative regret and convergence rates, demonstrating their ability to mitigate the impact of these attacks and continue effective learning in compromised environments. Quantitative results show that these algorithms exhibit a limited performance degradation compared to benign settings, indicating a significant level of resilience to adversarial manipulation.

Evaluations conducted within a Byzantine Decentralized Setting demonstrate the robustness of the DeMABAR algorithm when operating with faulty agents. These simulations specifically assess DeMABAR’s ability to maintain consistent performance despite the presence of agents exhibiting arbitrary, malicious, or otherwise unpredictable behavior. Results indicate that DeMABAR can reliably converge towards optimal solutions even when a subset of agents provide incorrect or misleading information, confirming its resilience in decentralized environments prone to agent failure or compromise. The algorithm’s performance is quantified by its ability to minimize cumulative regret, even under these adversarial conditions.

DeMABAR, a novel algorithm presented in this work, achieves a regret bound of $O(\sqrt{T}ln(T)/V)$ across both adversarial corruption and Byzantine failure scenarios. This performance metric indicates the algorithm’s capacity to minimize the difference between its cumulative reward and the optimal cumulative reward over time, $T$, while accounting for the influence of malicious or faulty agents represented by parameter $V$. The consistent attainment of this bound in both compromised environments demonstrates DeMABAR’s robustness and effectiveness in maintaining low regret, signifying a high level of performance even when faced with unreliable or actively malicious participants.

Parameter $\lambda$ functions as a critical hyperparameter in the tested algorithms, directly influencing the exploration-exploitation trade-off. Higher values of $\lambda$ prioritize exploitation by favoring actions with previously observed high rewards, potentially leading to faster convergence but increased vulnerability to adversarial or Byzantine influences. Conversely, lower values of $\lambda$ encourage greater exploration of the action space, enhancing robustness against malicious agents or faulty data but potentially slowing down the learning process. Empirical results consistently demonstrate that optimal performance in both the Adversarial Corruption and Byzantine Decentralized Settings is achieved through careful tuning of $\lambda$, highlighting its sensitivity and importance for maintaining low regret and reliable operation in compromised multi-agent environments.

Implications and Future Trajectories

The advancement of resilient multi-agent learning holds transformative potential across diverse fields. In robotics, these algorithms promise coordinated swarms capable of complex tasks like search and rescue, or collaborative construction, even when faced with individual robot failures. Autonomous vehicle systems stand to benefit from improved traffic flow and safety through decentralized coordination, minimizing congestion and reacting effectively to erratic driver behavior. Beyond these, efficient resource allocation – spanning energy grids, supply chains, and financial markets – becomes achievable through agents that learn to cooperate and compete robustly, optimizing distribution and minimizing waste. This paradigm extends to scenarios requiring decentralized decision-making, promising more adaptable and reliable systems than traditional, centralized approaches.

The dependable operation of multi-agent systems hinges on their resilience to compromised or malfunctioning components. As these systems become increasingly integrated into critical infrastructure – from automated transportation networks to smart grids and collaborative robotics – the potential consequences of a single rogue agent escalate dramatically. Consequently, research prioritizing the identification and neutralization of malicious or faulty agents isn’t merely about improving efficiency, but about guaranteeing safety and preventing catastrophic failures. Robust mitigation strategies, capable of isolating compromised entities and maintaining overall system integrity, are therefore paramount for building trust and enabling the widespread adoption of these increasingly complex technologies. The ability to function reliably despite the presence of adversarial or unpredictable agents represents a fundamental requirement for realizing the full potential of multi-agent systems in real-world applications.

DeMABAR distinguishes itself through exceptional communication efficiency, achieving a communication cost of $O(Vln(VT))$ – a significant advancement in multi-agent learning. This notation represents the algorithm’s scalability; $V$ denotes the number of agents, and $T$ signifies the time horizon. Crucially, the logarithmic component, $ln(VT)$, ensures that communication overhead grows much slower than the number of agents or the duration of the task. This minimized communication burden allows DeMABAR to operate effectively in bandwidth-constrained environments and with larger agent populations, outperforming alternative algorithms that often suffer from exponential communication scaling. The result is a system capable of robust, distributed decision-making without being hampered by excessive data exchange.

Advancing multi-agent learning necessitates algorithms capable of responding to unpredictable system degradation caused by compromised or malfunctioning agents; therefore, future work will prioritize the development of dynamic exploration strategies. These adaptive algorithms will continuously monitor agent behavior, assessing the prevalence of corrupt or Byzantine influences—agents that intentionally provide false information or act maliciously. By quantifying the degree of system corruption, the algorithms can intelligently adjust their exploration-exploitation balance, increasing exploratory actions when distrust is high to identify and isolate faulty agents, and reverting to more efficient exploitation strategies as system integrity is restored. This proactive approach promises to enhance the robustness and reliability of multi-agent systems operating in uncertain and potentially adversarial environments, moving beyond static defenses to embrace a continually learning and adapting framework.

The pursuit of DeMABAR, as detailed in the study, embodies a dedication to paring away unnecessary complexity. The algorithm’s strength isn’t in adding layers of defense, but in streamlining the core principles of exploration and exploitation to withstand both adversarial corruption and Byzantine failures. This aligns with the sentiment expressed by Carl Friedrich Gauss: “If I have seen further it is by standing on the shoulders of giants.” DeMABAR doesn’t reinvent bandit algorithms; it refines them, building upon established foundations to achieve resilience through focused, robust communication—a testament to the power of subtraction in achieving true innovation. The simplification inherent in its design isn’t a limitation, but its greatest asset.

What Remains?

The pursuit of Byzantine-resilient multi-agent bandit algorithms, as exemplified by DeMABAR, invariably circles back to a fundamental tension. Robustness is not achieved by adding layers of complexity, but by stripping away unnecessary assumptions. The current work offers a notable advance, yet the remaining challenge isn’t simply scaling to larger agent populations or more malicious adversaries. It is, instead, acknowledging the inherent limits of decentralized learning itself.

Future inquiry should not prioritize ever-more-sophisticated corruption models, but instead concentrate on the conditions under which any decentralized solution is viable. The problem is not merely ‘how to tolerate’ bad actors, but ‘how much tolerance is reasonable?’ A truly minimal approach will demand a rigorous analysis of the information loss inherent in distributed systems, and the irreducible cost of achieving consensus.

Ultimately, the field will progress not by building better algorithms, but by more precisely defining the problem. A clear understanding of what can, and more importantly cannot, be learned in a decentralized, adversarial environment—that is the true horizon. The elegance, as always, will reside in what is left unsaid.

Original article: https://arxiv.org/pdf/2511.10344.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2025-11-16 15:53