Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)
- URL: http://arxiv.org/abs/2302.09551v4
- Date: Wed, 07 May 2025 09:25:34 GMT
- Title: Auto.gov: Learning-based Governance for Decentralized Finance (DeFi)
- Authors: Jiahua Xu, Yebo Feng, Daniel Perez, Benjamin Livshits,
- Abstract summary: "Auto$.$gov" is a learning-based governance framework that employs a deep Qnetwork reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments.<n>In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold.
- Score: 14.697580721893809
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Decentralized finance (DeFi) is an integral component of the blockchain ecosystem, enabling a range of financial activities through smart-contract-based protocols. Traditional DeFi governance typically involves manual parameter adjustments by protocol teams or token holder votes, and is thus prone to human bias and financial risks, undermining the system's integrity and security. While existing efforts aim to establish more adaptive parameter adjustment schemes, there remains a need for a governance model that is both more efficient and resilient to significant market manipulations. In this paper, we introduce "Auto$.$gov", a learning-based governance framework that employs a deep Qnetwork (DQN) reinforcement learning (RL) strategy to perform semi-automated, data-driven parameter adjustments. We create a DeFi environment with an encoded action-state space akin to the Aave lending protocol for simulation and testing purposes, where Auto$.$gov has demonstrated the capability to retain funds that would have otherwise been lost to price oracle attacks. In tests with real-world data, Auto$.$gov outperforms the benchmark approaches by at least 14% and the static baseline model by tenfold, in terms of the preset performance metric--protocol profitability. Overall, the comprehensive evaluations confirm that Auto$.$gov is more efficient and effective than traditional governance methods, thereby enhancing the security, profitability, and ultimately, the sustainability of DeFi protocols.
Related papers
- Information-Theoretic Decentralized Secure Aggregation with Collusion Resilience [98.31540557973179]
We study the problem of decentralized secure aggregation (DSA) from an information-theoretic perspective.<n>We characterize the optimal rate region, which specifies the minimum achievable communication and secret key rates for DSA.<n>Our results establish the fundamental performance limits of DSA, providing insights for the design of provably secure and communication-efficient protocols.
arXiv Detail & Related papers (2025-08-01T12:51:37Z) - Agentar-Fin-R1: Enhancing Financial Intelligence through Domain Expertise, Training Efficiency, and Advanced Reasoning [12.548390779247987]
We introduce the Agentar-Fin-R1 series of financial large language models.<n>Our optimization approach integrates a high-quality, systematic financial task label system.<n>Our models undergo comprehensive evaluation on mainstream financial benchmarks.
arXiv Detail & Related papers (2025-07-22T17:52:16Z) - Cost-Optimal Active AI Model Evaluation [71.2069549142394]
Development of generative AI systems requires continual evaluation, data acquisition, and annotation.<n>We develop novel, cost-aware methods for actively balancing the use of a cheap, but often inaccurate, weak rater.<n>We derive a family of cost-optimal policies for allocating a given annotation budget between weak and strong raters.
arXiv Detail & Related papers (2025-06-09T17:14:41Z) - Accelerating RL for LLM Reasoning with Optimal Advantage Regression [52.0792918455501]
We propose a novel two-stage policy optimization framework that directly approximates the optimal advantage function.<n>$A$*-PO achieves competitive performance across a wide range of mathematical reasoning benchmarks.<n>It reduces training time by up to 2$times$ and peak memory usage by over 30% compared to PPO, GRPO, and REBEL.
arXiv Detail & Related papers (2025-05-27T03:58:50Z) - Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning [0.3376269351435395]
This paper applies deep reinforcement learning (DRL) to optimize liquidity provision in a DeFi protocol.
By promoting more efficient liquidity management, this work aims to make DeFi markets more accessible and inclusive for a broader range of participants.
arXiv Detail & Related papers (2025-01-13T17:27:11Z) - CryptoFormalEval: Integrating LLMs and Formal Verification for Automated Cryptographic Protocol Vulnerability Detection [41.94295877935867]
We introduce a benchmark to assess the ability of Large Language Models to autonomously identify vulnerabilities in new cryptographic protocols.
We created a dataset of novel, flawed, communication protocols and designed a method to automatically verify the vulnerabilities found by the AI agents.
arXiv Detail & Related papers (2024-11-20T14:16:55Z) - Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection [21.003217781832923]
This paper proposes an Automated Machine Learning (AutoML)-based autonomous IDS framework towards achieving autonomous cybersecurity for next-generation networks.
The proposed AutoML-based IDS was evaluated on two public benchmark network security datasets, CICIDS 2017 and 5G-NIDD.
This research marks a significant step towards fully autonomous cybersecurity in next-generation networks, potentially revolutionizing network security applications.
arXiv Detail & Related papers (2024-09-05T00:36:23Z) - Poisoning Attacks on Federated Learning-based Wireless Traffic Prediction [4.968718867282096]
Federated Learning (FL) offers a distributed framework to train a global control model across multiple base stations.
This makes it ideal for applications like wireless traffic prediction (WTP), which plays a crucial role in optimizing network resources.
Despite its promise, the security aspects of FL-based distributed wireless systems, particularly in regression-based WTP problems, remain inadequately investigated.
arXiv Detail & Related papers (2024-04-22T17:50:27Z) - Enhancing Security in Federated Learning through Adaptive
Consensus-Based Model Update Validation [2.28438857884398]
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks.
We propose a consensus-based verification process integrated with an adaptive thresholding mechanism.
Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
arXiv Detail & Related papers (2024-03-05T20:54:56Z) - A Survey and Comparative Analysis of Security Properties of CAN Authentication Protocols [92.81385447582882]
The Controller Area Network (CAN) bus leaves in-vehicle communications inherently non-secure.
This paper reviews and compares the 15 most prominent authentication protocols for the CAN bus.
We evaluate protocols based on essential operational criteria that contribute to ease of implementation.
arXiv Detail & Related papers (2024-01-19T14:52:04Z) - MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot
Learning [52.101643259906915]
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations.
Existing model-based offline RL methods are not suitable for offline-to-online fine-tuning in high-dimensional domains.
We propose an on-policy model-based method that can efficiently reuse prior data through model-based value expansion and policy regularization.
arXiv Detail & Related papers (2024-01-06T21:04:31Z) - Towards Evaluating Transfer-based Attacks Systematically, Practically,
and Fairly [79.07074710460012]
adversarial vulnerability of deep neural networks (DNNs) has drawn great attention.
An increasing number of transfer-based methods have been developed to fool black-box DNN models.
We establish a transfer-based attack benchmark (TA-Bench) which implements 30+ methods.
arXiv Detail & Related papers (2023-11-02T15:35:58Z) - Defending Against Poisoning Attacks in Federated Learning with
Blockchain [12.840821573271999]
We propose a secure and reliable federated learning system based on blockchain and distributed ledger technology.
Our system incorporates a peer-to-peer voting mechanism and a reward-and-slash mechanism, which are powered by on-chain smart contracts, to detect and deter malicious behaviors.
arXiv Detail & Related papers (2023-07-02T11:23:33Z) - Confidence-Controlled Exploration: Efficient Sparse-Reward Policy Learning for Robot Navigation [72.24964965882783]
Reinforcement learning (RL) is a promising approach for robotic navigation, allowing robots to learn through trial and error.
Real-world robotic tasks often suffer from sparse rewards, leading to inefficient exploration and suboptimal policies.
We introduce Confidence-Controlled Exploration (CCE), a novel method that improves sample efficiency in RL-based robotic navigation without modifying the reward function.
arXiv Detail & Related papers (2023-06-09T18:45:15Z) - When Authentication Is Not Enough: On the Security of Behavioral-Based Driver Authentication Systems [53.2306792009435]
We develop two lightweight driver authentication systems based on Random Forest and Recurrent Neural Network architectures.
We are the first to propose attacks against these systems by developing two novel evasion attacks, SMARTCAN and GANCAN.
Through our contributions, we aid practitioners in safely adopting these systems, help reduce car thefts, and enhance driver security.
arXiv Detail & Related papers (2023-06-09T14:33:26Z) - A Data-driven Pricing Scheme for Optimal Routing through Artificial
Currencies [1.3419982985275638]
Mobility systems often suffer from a high price of anarchy due to the uncontrolled behavior of selfish users.
This paper presents a data-driven approach to automatically adapt artificial-currency tolls within repetitive-game settings.
arXiv Detail & Related papers (2022-11-27T11:23:29Z) - A Control Theoretic Approach to Infrastructure-Centric Blockchain
Tokenomics [7.353066706896901]
This paper argues that token economies for infrastructure networks should be structured differently.
New suppliers should continually join the network to provide services and support to the ecosystem.
To achieve such an equilibrium, the decentralized token economy should be adaptable and controllable.
arXiv Detail & Related papers (2022-10-23T23:23:13Z) - Fully Decentralized Model-based Policy Optimization for Networked
Systems [23.46407780093797]
This work aims to improve data efficiency of multi-agent control by model-based learning.
We consider networked systems where agents are cooperative and communicate only locally with their neighbors.
In our method, each agent learns a dynamic model to predict future states and broadcast their predictions by communication, and then the policies are trained under the model rollouts.
arXiv Detail & Related papers (2022-07-13T23:52:14Z) - On Effective Scheduling of Model-based Reinforcement Learning [53.027698625496015]
We propose a framework named AutoMBPO to automatically schedule the real data ratio.
In this paper, we first theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance.
arXiv Detail & Related papers (2021-11-16T15:24:59Z) - Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in
Edge Industrial IoT [106.83952081124195]
Reinforcement learning (RL) has been widely investigated and shown to be a promising solution for decision-making and optimal control processes.
We propose an adaptive ADMM (asI-ADMM) algorithm and apply it to decentralized RL with edge-computing-empowered IIoT networks.
Experiment results show that our proposed algorithms outperform the state of the art in terms of communication costs and scalability, and can well adapt to complex IoT environments.
arXiv Detail & Related papers (2021-06-30T16:49:07Z) - Safe RAN control: A Symbolic Reinforcement Learning Approach [62.997667081978825]
We present a Symbolic Reinforcement Learning (SRL) based architecture for safety control of Radio Access Network (RAN) applications.
We provide a purely automated procedure in which a user can specify high-level logical safety specifications for a given cellular network topology.
We introduce a user interface (UI) developed to help a user set intent specifications to the system, and inspect the difference in agent proposed actions.
arXiv Detail & Related papers (2021-06-03T16:45:40Z) - Federated Learning on the Road: Autonomous Controller Design for
Connected and Autonomous Vehicles [109.71532364079711]
A new federated learning (FL) framework is proposed for designing the autonomous controller of connected and autonomous vehicles (CAVs)
A novel dynamic federated proximal (DFP) algorithm is proposed that accounts for the mobility of CAVs, the wireless fading channels, and the unbalanced and nonindependent and identically distributed data across CAVs.
A rigorous convergence analysis is performed for the proposed algorithm to identify how fast the CAVs converge to using the optimal controller.
arXiv Detail & Related papers (2021-02-05T19:57:47Z) - Bayesian Optimization with Machine Learning Algorithms Towards Anomaly
Detection [66.05992706105224]
In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique.
The performance of the considered algorithms is evaluated using the ISCX 2012 dataset.
Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
arXiv Detail & Related papers (2020-08-05T19:29:35Z) - Regulation conform DLT-operable payment adapter based on trustless -
justified trust combined generalized state channels [77.34726150561087]
Economy of Things (EoT) will be based on software agents running on peer-to-peer trustless networks.
We give an overview of current solutions that differ in their fundamental values and technological possibilities.
We propose to combine the strengths of the crypto based, decentralized trustless elements with established and well regulated means of payment.
arXiv Detail & Related papers (2020-07-03T10:45:55Z) - Privacy-preserving Traffic Flow Prediction: A Federated Learning
Approach [61.64006416975458]
We propose a privacy-preserving machine learning technique named Federated Learning-based Gated Recurrent Unit neural network algorithm (FedGRU) for traffic flow prediction.
FedGRU differs from current centralized learning methods and updates universal learning models through a secure parameter aggregation mechanism.
It is shown that FedGRU's prediction accuracy is 90.96% higher than the advanced deep learning models.
arXiv Detail & Related papers (2020-03-19T13:07:49Z) - Adaptive Control and Regret Minimization in Linear Quadratic Gaussian
(LQG) Setting [91.43582419264763]
We propose LqgOpt, a novel reinforcement learning algorithm based on the principle of optimism in the face of uncertainty.
LqgOpt efficiently explores the system dynamics, estimates the model parameters up to their confidence interval, and deploys the controller of the most optimistic model.
arXiv Detail & Related papers (2020-03-12T19:56:38Z) - Towards Probabilistic Verification of Machine Unlearning [30.892906429582904]
We propose a formal framework to study the design of verification mechanisms for data deletion requests.
We show that our approach has minimal effect on the machine learning service's accuracy but provides high confidence verification of unlearning.
arXiv Detail & Related papers (2020-03-09T16:39:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.