Federated Learning Enhanced Blockchain Interoperability Protocol for Intelligent Cross-Chain Transaction Validation

Abstract The proliferation of heterogeneous blockchain networks has created an urgent need for efficient cross-chain interoperability solutions. Current cross-chain protocols face significant challenges in intelligent routing and suffer from trust issues, leading to inefficient transaction processing and security vulnerabilities. This research introduces a novel Federated Learning-Enhanced Blockchain Interoperability Protocol (FL-BIP) that integrates distributed machine learning techniques with cross-chain validation mechanisms. Our approach leverages federated learning algorithms to enable collaborative learning across heterogeneous blockchain networks without compromising data privacy. The proposed protocol implements a distributed validation framework that learns from historical transaction patterns to optimize routing decisions and enhance security measures. Experimental results demonstrate a 52.3% reduction in cross-chain transaction time compared to existing protocols, with improved security metrics including 94.7% attack detection accuracy and 15.2% reduction in false positives. The FL-BIP protocol successfully addresses the research gap in intelligent cross-chain transaction validation while maintaining decentralization principles and preserving network autonomy. Our contribution provides a scalable solution for blockchain interoperability that adapts to network dynamics and evolving threat landscapes through continuous federated learning Key Words: Federated Learning, Blockchain Interoperability, Cross-Chain Protocols, Distributed Validation, Machine Learning, Cryptocurrency, Smart Contracts, Consensus Mechanisms