arXiv:2611. 05586v1 Announce Type: new Abstract: As autonomous AI agents enter permissionless onchain markets, selecting new service providers remains a core challenge.
By Reza Ramji, Preston Vander Vos, Ziming Zeng, Duc V. Le
DeFiRank is a federated learning-based pairwise ranking framework designed to improve governance in decentralized financial networks. It allows multiple institutions to collaboratively train a ranking model without sharing private transaction data, capturing network-wide behavioural patterns such as diversity of counterparties, multi-application usage, and transaction frequency. Evaluated on a real-world blockchain dataset, DeFiRank achieves ranking performance close to a centralized baseline while preserving data privacy and maintaining ranking stability, enabling reliable provider promotion and demotion decisions.
By Zahra Batool, Michael Duchesne, Kaiwen Zhang, Ulrich Aivodji
arXiv:2606. 18098v1 Announce Type: new Abstract: Advances in Artificial Intelligence (AI) have led AI for Theorem Proving to become a promising means of formally verifying computer systems.
By Elliot Jones, William Knottenbelt
arXiv:2602. 08290v2 Announce Type: replace-cross Abstract: In federated learning (FL), decentralized model training allows multi-ple participants to collaboratively improve a shared machine learning model without exchanging raw data.
By Ajay Kumar Shrestha
arXiv:2609.37819v1 Announce Type: cross
Abstract: Electronic invoices are replacing paper invoices worldwide, but today's centralized architectures leave three problems unsolved on the consumption si...
By Jia Cai
arXiv:2609.15521v1 Announce Type: new
Abstract: Federated learning enables multiple parties to train a shared model without centralizing raw data with the help of an aggregator, but introduces integr...
By Doryan Lesaignoux, Enrique M\'armol Campos, Gabriele Spini, Jos\'e L. Hern\'andez-Ramos, Stephan Krenn