DECS tool assesses swap rates of DEXes and Fusion outperforms competitors.
problem Lack of unbiased swap rate comparisons in decentralized finance.
method Swap transaction monitoring and simulation techniques.
result 1inch Classic and Fusion consistently outperform competitors in swap rates.
This paper analyzes voter coalitions in MakerDAO's decentralized governance.
problem Understanding the governance structure and influence of voter coalitions in DAOs.
method Applied clustering algorithm to voting history of MakerDAO to identify voter coalitions.
result The emergence of a dominant voter coalition signals governance centralization in DAOs.
Algorithm for agents to agree on a single objective in multi-task networks.
problem Decentralized decision-making in multi-task networks with multiple objectives.
method Distributed decision-making algorithm for agents observing different models.
result Agents reach agreement on which model to track for network performance enhancement.
Review of recent advances in decentralized MARL with networked agents.
problem Sequential decision-making by networked agents without central coordination.
method Analysis of recent algorithms and their theoretical foundations.
result Inspiring more research in this challenging area.
This article reviews recent advances in secure distributed and decentralized inference and learning against Byzantine threats.
problem Securing distributed and decentralized inference and learning against malicious attacks.
method Review of recent algorithmic approaches under Byzantine threat model.
result A plethora of robust algorithmic approaches have been developed.
A decentralized approach for agents to learn and optimize collectively.
problem Challenges in coordinating non-cooperative agents to solve complex sequential decision problems.
method Designing a learning environment where agents learn by trading and optimizing local objectives, leading to a Nash equilibrium.
result Decentralized reinforcement learning algorithms that can handle various decision-making scenarios.
Framework for responsible LLM deployment with human involvement and decentralized technologies.
problem Challenges in deploying LLMs for high-stakes decisions, including data security and accountability.
method Interactive human involvement through multiple iterations, decentralized technologies, and automated auditing.
result Enhanced security and accountability in LLM deployment for financial decisions.
Partner-aware algorithms improve AI collaboration in multi-agent settings.
problem Improving AI cooperation in teams with shared rewards.
method Proposed Partner-Aware strategy extending Upper Confidence Bound for decentralized MAB.
result Achieves logarithmic regret in collaborative decision-making.
The paper addresses private and Byzantine-proof cooperative decision-making in multi-agent systems.
problem Designing algorithms for multi-agent decision-making that are private and resilient to faulty agents.
method Upper-confidence bound algorithms for stochastic bandit problems under privacy and Byzantine conditions.
result Optimal regret achieved in both private and Byzantine-tolerant settings.
This paper surveys methods to handle non-stationarity in multi-agent deep reinforcement learning.
problem Non-stationarity in multi-agent reinforcement learning environments.
method Modifications in training procedures, opponent policy representation learning, meta-learning, communication, and decentralized learning.
result A comprehensive review of recent works on addressing non-stationarity in multi-agent deep reinforcement learning.
Quantum self-attention boosts automated market maker performance in crypto trading.
problem Improving automated market maker rebalancing in crypto trading.
method Quantum Adaptive Self-Attention (QASA) using variational quantum circuits and softmax attention.
result QASA-Sequence variant achieves best single-model risk-adjusted performance in crypto trading.
This study compares decentralized banks and finds some lack decentralization.
problem Decentralized banks do not fully decentralize transactions as expected.
method Network analysis of transaction data from four banks using core-periphery features.
result MakerDao and Compound are more decentralized than Aave and Liquity.
Study on learning strategies in matching markets with uncertain preferences.
problem Decision-making in scarcity of shared resources with unknown agent preferences.
method Representation of preferences in a reproducing kernel Hilbert space, learning algorithm for uncertainty.
result Optimal strategies derived to maximize agents' expected payoffs, with stability and fairness properties.
Systematizes blockchain decentralization taxonomy and metrics.
problem Lack of a unified definition for blockchain decentralization.
method Formulated a taxonomy of five facets and developed metrics.
result Provided comprehensive insights into blockchain decentralization.
DORIS algorithm achieves no-regret learning in Markov games with adversarial opponents.
problem Decentralized policy learning in Markov games with nonstationary opponents.
method DORIS algorithm using optimistic hyperpolicy mirror descent.
result Achieves K \sqrt{K} K -regret in general function approximation. Study improves cryptocurrency volatility forecasting using multiple data sources.
problem Improving accuracy of predicting cryptocurrency volatility.
method Developed CoMForE, a multimodal AdaBoost-LSTM ensemble model.
result Significantly improved cryptocurrency volatility forecasting (19.29% improvement).
Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, these methods rely on a concatenation of agent states to represent the information content required for decentralized decision making. However, concatenation scales poorly to swarm systems with a large…
New algorithm reduces complexity in multi-agent reinforcement learning.
problem High computational complexity in exact computations for multi-agent reinforcement learning.
method Design of a scalable algorithm based on Natural Policy Gradient, using local information and limited communication.
result Converges to globally optimal policy with dimension-free complexity and localization error.
Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.
problem Understanding the actual decentralization in DeFi token transactions on the Ethereum blockchain.
method Applied social network analysis to measure decentralization in Aave token transactions.
result A more decentralized Aave token network predicts higher returns and lower volatility.
MakerDAO's governance is centralized despite its decentralized claim.
problem Decentralization illusion in Decentralized Finance (DeFi) governance.
method Empirical analysis using financial, transaction, network, and sentiment indicators.
result Centralized governance impacts Maker protocol and voting power distribution.
Decentralized finance uses blockchain for $70B in assets, differing from traditional finance.
problem Ensuring compliance and security in decentralized finance.
method Systematic analysis of legal, economic, security, and privacy aspects.
result Decentralized finance offers unique economic effects and security features.
Study on decentralization in DAOs and its effect on financial efficiency in DeFi.
problem Understanding the impact of decentralization on financial efficiency in blockchain-based governance.
method Analysis using Gini coefficient as an inequality indicator, comparing ROI of token owners.
result Real decentralization affects financial efficiency positively in DeFi.
The paper analyzes stability and generalization of decentralized SGD.
problem Stability and generalization of decentralized stochastic gradient descent.
method Novel formulation of decentralized stochastic gradient descent combined with non/convex optimization theory.
result First stability and generalization guarantees for decentralized stochastic gradient descent.
NFTs raise concerns like scams, racism, and sexism; centralization vs decentralization debate.
problem Concerns and value judgments of stakeholders in NFT market.
method Mixed quantitative and qualitative methods: social media analysis and interviews.
result Identified financial scams, counterfeit NFTs, hacking, and unethical NFTs as major issues.
Proposes a new algorithm to reduce communication costs in decentralized training.
problem How to apply error-compensated compression to decentralized training.
method Error-compensated stochastic gradient descent for decentralized training.
result Proposed algorithm outperforms existing methods in communication cost reduction.
A decentralized approach for multi-source domain adaptation.
problem Transfer knowledge from multiple related domains to an unlabeled target domain.
method Federated Dataset Dictionary Learning (FedDaDiL) framework, eliminating central server, using Wasserstein barycenters.
result Our decentralized approach effectively adapts source domains to an unlabeled target domain.
D-SPIDER-SFO solves nonconvex optimization problems faster on decentralized networks.
problem Finding a decentralized algorithm with similar convergence rate to SPIDER-SFO.
method Proposed D-SPIDER-SFO, a decentralized variant of SPIDER-SFO.
result Achieves a similar gradient computation cost to centralized SPIDER-SFO.
Decentralized Gaussian processes for multi-agent learning.
problem Training and prediction in multi-agent systems.
method Decentralized ADMM for GP hyper-parameter training and iterative consensus for prediction.
result Subset of agents can perform predictions using covariance-based nearest neighbor selection.
Paper tightens lower bounds on decentralized training complexity.
problem Understanding and optimizing iteration complexity in decentralized training.
method Proved a tight lower bound on iteration complexity and proposed DeTAG algorithm.
result DeTAG achieves the theoretical lower bound with only a logarithmic gap.
This study measures decentralization in blockchain finance governance.
problem Defining and measuring decentralization in blockchain finance applications.
method Aggregating and analyzing empirical data of four finance applications to calculate coefficients for governance token distribution.
result Gauges for objective evaluation of token governance capabilities and limitations.
Paper proposes decentralized annuities for better retirement security.
problem Current pension systems' limitations and fairness issues.
method Theoretical models and fairness concepts analysis.
result Decentralized annuities offer enhanced flexibility and social welfare.
Scores political leanings in Web3 betting markets.
problem Understanding political motivations in decentralized prediction markets.
method Constructing PBLS from Polymarket data, analyzing 15k addresses, 4k events, 8k markets.
result Validated PBLS through internal and external comparisons, revealing political and profit motives.
Study shows skewed data labels significantly impact decentralized ML accuracy.
problem Skewed data labels across devices/locations cause significant accuracy loss in decentralized ML.
method Detailed experimental study on skewed data labels, presenting SkewScout system-level approach.
result Skewed data labels are a fundamental challenge for decentralized learning, affecting many applications and models.
New algorithm tackles multi-player bandit problems with limited access to arms.
problem Limited access to dynamic local subsets of arms in multi-player multi-armed bandit problems.
method Adopted Upper Confidence Bound (UCB) for exploration-exploitation and distributed optimization for collisions.
result Proposes a decentralized algorithm with near-optimal regret guarantee.
Enhances parallelism in decentralized learning for larger networks.
problem Scalability limitations in decentralized learning with increasing number of machines.
method Proposes Decentralized Anytime SGD, a novel algorithm that extends parallelism threshold.
result Establishes a theoretical upper bound on parallelism surpassing current state-of-the-art.
Novel periodic momentum SGD method for decentralized training with linear speedup.
problem Lack of effective momentum schema in decentralized training methods.
method Proposes a novel periodic decentralized momentum SGD method.
result Achieves linear speedup in decentralized training.
BRIDGE improves decentralized learning resilience against Byzantine failures.
problem Decentralized learning in distributed systems is vulnerable to Byzantine failures.
method Introduces BRIDGE, a scalable Byzantine-resilient decentralized gradient descent algorithm.
result Proves algorithmic and statistical convergence guarantees for BRIDGE.
Single global merging boosts decentralized learning performance.
problem Limited communication in decentralized learning hinders performance.
method Scheduled communication, focusing on final step with global merging.
result Single global merging improves global test performance.
Bayesian algorithms minimize cumulative regret in decentralized multi-agent bandits.
problem Minimizing cumulative regret in a decentralized multi-agent multi-armed bandit problem.
method Proposed decentralized Bayesian multi-armed bandit framework, including Thompson Sampling and Bayes-UCB algorithms.
result Regret scales logarithmically with constants matching those of an optimal centralized agent.
Unified framework for decentralized bilevel optimization with various heterogeneity-correction strategies.
problem Decentralized bilevel optimization with neighborhood communications and data heterogeneity.
method SPARKLE: Single-loop Primal-dual Algorithm for decentralized bilevel optimization, incorporating various heterogeneity-correction techniques.
result Unified convergence analysis for SPARKLE with state-of-the-art convergence rates compared to existing algorithms.
Flexible decentralized MARL framework for cooperative multi-agent learning.
problem Complexity and impracticality of centralized MARL in complicated applications.
method Flexible fully-decentralized actor-critic MARL framework using primal-dual hybrid gradient descent.
result Competitive performance in large-scale cooperative multi-agent environments.
New algorithms for Bayesian inference in decentralized learning.
problem Bayesian inference in decentralized learning settings.
method Decentralized SGLD and Decentralized SGHMC.
result Convergence of iterates to target distribution in 2-Wasserstein distance.
This research compiles knowledge on decentralized exchanges with AMM protocols.
problem Improving and developing AMM-based decentralized exchanges.
method Established a general AMM framework, compared mechanics, discussed security and privacy.
result Illustrated conservation and slippage functions of AMM protocols.
This paper improves privacy accounting in decentralized FL using f-Differential Privacy.
problem Challenges in accurately quantifying privacy budget in decentralized FL.
method Develops two new f-DP-based accounting methods for decentralized FL.
result Yields tighter (ε,δ) bounds and improved utility compared to existing methods.
Decentralized method solves saddle point problems with theoretical guarantees.
problem Solving saddle point problems in a decentralized network.
method Proximal point method adapted for decentralized networks.
result Converges to approximate stationarity with rate of O(1/√T).
DE-PSGLD samples from constrained distributions in a decentralized manner.
problem Sampling from log-concave distributions with constraints.
method Decentralized Proximal Stochastic Gradient Langevin Dynamics with proximal regularization.
result DE-PSGLD converges to a regularized Gibbs distribution and maintains posterior concentration.
A framework for decentralized optimization using first-order methods.
problem Optimization of finite sums over networked nodes.
method Decentralized first-order gradient and stochastic methods.
result General framework for undirected and directed networks.
Graph neural networks learn decentralized controllers from data.
problem Finding optimal decentralized controllers for autonomous agents is challenging.
method Adapting graph neural networks to handle delayed communications and ensure scalability and transferability.
result Graph neural networks can learn decentralized controllers from data, addressing the scalability and practical implementation issues of centralized controllers.