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.
arXiv:2410. 11378v3 Announce Type: replace-cross Abstract: Personalized collaborative learning in federated settings faces a critical trade-off between customization and participant trust.
arXiv:2611. 05586v1 Announce Type: new Abstract: As autonomous AI agents enter permissionless onchain markets, selecting new service providers remains a core challenge.
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.
arXiv:2606. 26028v2 Announce Type: replace-cross Abstract: As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy?
arXiv:2607. 08651v1 Announce Type: new Abstract: Decentralized federated learning (DFL) removes the central server by letting nodes exchange model updates through peer-to-peer gossip, but existing gossip-based methods often lack provenance finality and resilience to Byzantine or lazy participants.
The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task for fraud and clinical‑risk scoring, showing that plain FedAvg fails when a quarter of twenty clients are Byzantine, while DP‑BR‑FedAvg recovers more signal and bounds privacy loss. The study demonstrates that privacy and robustness interact, and system design for regulated, adversarial, cross‑institutional settings must account for this interaction.
As autonomous AI agents increasingly transact across organizational boundaries, a fundamental trust challenge emerges: how can an agent assess whether an unknown counterpart is trustworthy? The ERC-8004 protocol addresses this challenge with the first permissionless trust layer for AI agent economies, built around three on-chain registries for Identity, Reputation, and Validation.
The paper introduces DP‑BR‑FedAvg, a federated learning framework that combines Gaussian‑mechanism differential privacy with a coordinate‑wise trimmed‑mean Byzantine‑robust aggregation rule. It is evaluated on a simulated cross‑institutional classification task involving fraud and clinical‑risk scoring, where it improves the F1‑score for a minority class from 0.030 (plain FedAvg) to 0.119 while bounding privacy loss. The study demonstrates that privacy and robustness mechanisms interact, and that system design for regulated, adversarial, cross‑institutional settings must account for this interaction.
The paper investigates when adding a second AI component—an AI supervisor—benefits network‑fraud decision making. Using a role‑aware Decider‑Supervisor framework with blockchain auditability, the authors evaluate four configurations combining centralized ML, federated meta‑models, and large language model variants. Results show that only specific setups (e.g., DS‑4 Base in extreme high‑fraud scenarios) reduce errors, while most supervised configurations either underperform or add operational burden.
arXiv:2606. 00947v1 Announce Type: cross Abstract: Foundation models are increasingly personalized on decentralized private data through federated learning and are now deployed at scale under growing regulatory requirements for post-market monitoring.
arXiv:2503. 07570v3 Announce Type: replace-cross Abstract: Deep learning, when integrated with a large amount of training data, has the potential to outperform machine learning in terms of high accuracy.
arXiv:2607. 27370v1 Announce Type: new Abstract: Sybil attackers are Blockchain actors that adopt the characteristics of regular users to exploit airdrops or influence governance.