arXiv research

Study optimal risk sharing in decentralized peer-to-peer markets with robust risk measures.

Biscotti uses blockchain for secure peer-to-peer ML without central coordination.

The paper analyzes reinsurance strategies in peer-to-peer insurance schemes.

The study improves credit evaluation in peer-to-peer lending using machine learning.

This paper builds a recommendation system for borrowers on P2PL platforms to lower interest rates.

Paper develops Byzantine-resilient algorithms for decentralized learning.

BrainTorrent uses peer-to-peer FL for medical image segmentation without a central server.

FedCoin uses blockchain to fairly distribute incentives in federated learning.

The rise of connected personal devices together with privacy concerns call for machine learning algorithms capable of leveraging the data of a large number of agents to learn personalized models under strong privacy requirements. In this paper, we introduce an efficient algorithm to address the above problem in a fully…

Decentralized learning over social networks using Bayesian methods.

We provide two distributed confidence ball algorithms for solving linear bandit problems in peer to peer networks with limited communication capabilities. For the first, we assume that all the peers are solving the same linear bandit problem, and prove that our algorithm achieves the optimal asymptotic regret rate of a…

This study compares decentralized banks and finds some lack decentralization.

DEX allows HFTs to quickly acquire speed, improving price discovery without sacrificing liquidity.

A decentralized online quantum cash system, called qBitcoin, is given. We design the system which has great benefits of quantization in the following sense. Firstly, quantum teleportation technology is used for coin transaction, which prevents from the owner of the coin keeping the original coin data even after sending…

This paper uses LSTM to predict P2P lending default rates, improving accuracy with macroeconomic data.

Deep neural networks reduce loan portfolio risk.

Two-stage scoring approach for P2P lending improves loan profitability prediction.

Paper tackles low sample and communication complexities in decentralized bilevel optimization.

DICE estimates data influence cascade in decentralized learning networks.

Decentralized learning of personalized models and collaboration graphs without central coordination.

Study compares financial and gambling markets, finding similarities and potential applications.

Study finds Aave token network has core-periphery structure, with high decentralization predicting better returns.

Paper proves PI consensus algorithm converges exponentially under restricted secant inequality.

We study analytically and numerically Minsky instability as a combination of top-down, bottom-up and peer-to-peer positive feedback loops. The peer-to-peer interactions are represented by the links of a network formed by the connections between firms, contagion leading to avalanches and percolation phase transitions pr…

Decentralized algorithm for non-convex problems converges faster than centralized methods.

A commonly expressed concern about the rise of the peer-to-peer rental market Airbnb is that hosts---those renting out their properties---impose costs on their unwitting neighbors. I consider the question of whether apartment building owners will, in a competitive rental market, set a building-specific Airbnb hosting p…

We generalize stochastic subgradient descent methods to situations in which we do not receive independent samples from the distribution over which we optimize, but instead receive samples that are coupled over time. We show that as long as the source of randomness is suitably ergodic---it converges quickly enough to a …

A new framework integrates credit scoring into profit scoring for better P2P lending investments.

A novel approach tackles sparse linear bandits with reduced communication costs and minimal cumulative regret.

GradientCoin aims to create a decentralized LLM system similar to Bitcoin.

Research tackles alliance formation in many-player zero-sum games, showing reinforcement learning fails but a contract mechanism can help.

Solomon and Golo [1] have recently proposed an autocatalytic (self-reinforcing) feedback model which couples a macroscopic system parameter (the interest rate), a microscopic parameter that measures the distribution of the states of the individual agents (the number of firms in financial difficulty) and a peer-to-peer …

Paper develops a private algorithm for multi-agent learning in bandits.

We consider a set of learning agents in a collaborative peer-to-peer network, where each agent learns a personalized model according to its own learning objective. The question addressed in this paper is: how can agents improve upon their locally trained model by communicating with other agents that have similar object…

Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique t…

Proposes a decentralized insurance protocol for DeFi.

This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…

Unified framework for Byzantine robust gossip algorithms with guaranteed performance.

Dark blockchain venues increase miners' profits but raise users' execution risk.

Decentralized learning over graphs improves model accuracy.

Investigates stochastic networks on disordered lattices, converging to Brownian web in 2D.

Bitcoins and Blockchain technologies are attracting the attention of different scientific communities. In addition, their widespread industrial applications and the continuous introduction of cryptocurrencies are also stimulating the attention of the public opinion. The underlying structure of these technologies consti…

In this chapter, we analyze nonlinear filtering problems in distributed environments, e.g., sensor networks or peer-to-peer protocols. In these scenarios, the agents in the environment receive measurements in a streaming fashion, and they are required to estimate a common (nonlinear) model by alternating local computat…

Human stablecoin transactions predict political risk in cryptocurrency markets.

Study predicts P2P lending platform failures using machine learning.

Fragmented exchanges arise due to speed advantages in high-activity regions.

Gossip-based actor-learner architectures improve deep reinforcement learning efficiency and scalability.

Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i.e., agents) for better scalability. However, a typical distributed system is usually implemented with a central server that collects data statis…