Evolving Collective Intelligence for Unmanned Marine Vehicle Swarms: A Federated Meta-Learning Framework for Cross-Fleet Planning and Control

Abstract

The development of robust autonomous maritime systems is fundamentally constrained by the “data silo” problem, where valuable operational data from disparate fleets remain isolated due to privacy concerns, severely limiting the scalability of general-purpose navigation intelligence. To address this barrier, we propose a novel Federated Meta-Transfer Learning (FMTL) framework that enables collaborative evolution of unmanned surface vehicle (USV) swarms while preserving data privacy. Our hierarchical approach orchestrates three synergistic stages: (1) transfer learning pre-trains a universal “Sea-Sense” foundation model on large-scale maritime data to establish fundamental navigation priors; (2) federated learning enables decentralized fleets to collaboratively refine this model through encrypted gradient aggregation, forming a distributed cognitive network; (3) meta-learning allows for rapid personalization to individual vessel dynamics with minimal adaptation trials. Comprehensive simulations across heterogeneous fleet distributions demonstrate that our federated model achieves a 95.4% average success rate across diverse maritime scenarios, significantly outperforming isolated specialist models (63.9–73.1%), while enabling zero-shot performance of 78.5% and few-shot adaptation within 8–12 episodes on unseen tasks. This work establishes a scalable, privacy-preserving paradigm for collective maritime intelligence through swarm-based learning.

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