Federated ranking system improves decentralized finance governance fairness
DeFiRank: Federated Pairwise Ranking for Governance in Decentralized Financial Networks
Artificial Intelligence
Summary
Decentralized financial networks often depend on human committees to decide which participants are trustworthy, but this can be slow and biased. The authors propose a new method called DeFiRank that lets multiple organizations work together to rank providers by their genuine activity, without sharing private data. This approach uses a type of machine learning across many private datasets to recognize helpful behavior patterns while keeping data secret. Their tests show it nearly matches the accuracy of centralized methods and can reliably promote or demote participants based on authentic network involvement.
What this means in practice
- •For blockchain platform operators: Implement privacy-preserving ranking systems to assess provider trustworthiness without accessing sensitive transaction data.
- •For financial fraud detection teams: Use federated learning models trained on diverse data to identify and demote suspicious participants more effectively across networks.
Authors
Zahra Batool, Michael Duchesne, Kaiwen Zhang, Ulrich Aivodji
Abstract
Existing governance approaches in decentralized financial networks rely on manual review processes conducted by human committees, which introduces subjective bias, creates a scalability bottleneck under high network volume, and fails to capture complex behavioural patterns and evolving network dynamics. To address these limitations, we propose \name, a federated learning-based pairwise ranking framework in which multiple institutions collaboratively train a ranking model without sharing their private transaction data. This collaborative learning enables the model to capture network-wide behavioural patterns that signify strong network participation and directly contribute to the network effect, including diversity of counterparties, usage of multiple applications, and frequency of transactions. The proposed system ranks providers based on the authenticity of their actions, promoting genuine contributors and demoting those exhibiting suspicious behaviour. We evaluate \name\ on a real-world blockchain transaction dataset. Results demonstrate that the federated approach achieves ranking performance modestly below the centralized baseline (NDCG@15: $\approx$0.87 vs.\ $\approx$0.92) while preserving data privacy and matching its ranking stability (Kendall $τ$: $\approx$0.70 for both regimes), producing reliable governance decisions, including provider promotion and demotion (648 and 39 unique providers, respectively), without requiring centralized access to sensitive transaction data.