Intelligent ship traffic supervision system based on distributed blockchain and federated reinforcement learning for collaborative decision optimization – Scientific Reports

A new multi-layer system promises safer, smarter, and more coordinated ship traffic management by fusing distributed blockchain with federated reinforcement learning (FRL). Designed for cross-jurisdictional maritime operations, it enables authorities to collaborate on decisions in real time without sharing raw data, preserving sovereignty while unlocking global intelligence.

Four-layer architecture

The platform is organized into four tightly coupled layers that balance scalability, security, and operational efficiency.

1) Data layer

This foundation ingests and normalizes heterogeneous sources—AIS, radar, satellite feeds, and port systems—using standardized schemas and protocols to ensure interoperability across regions. It supports streaming and batch pipelines, with KPIs focused on throughput, latency, data quality, and compute cost. Preprocessing includes deduplication, outlier handling, and time-space alignment to create a unified maritime situational picture.

2) Blockchain layer

A permissioned blockchain underpins trusted, tamper-resistant data and event exchange between authorities. A tailored PBFT consensus (with 7–21 validators) provides fault tolerance and low-latency finality, using parameters such as 2 MB block size, 3 s block interval, 5 s endorsement timeout, and 2 s batch timeout. Smart contracts enforce access control, audit trails, and verification logic. To balance security and efficiency, critical metadata and hashes remain on-chain while large payloads (e.g., radar imagery) reside off-chain with cryptographic references.

3) Federated learning layer

Authorities train models locally and share only privacy-preserving updates. Secure aggregation and differential privacy protect sensitive operations while enabling a global model to converge. Robust aggregation (e.g., trimmed mean or median) filters anomalous updates, defending against poisoning and byzantine behavior. Convergence is monitored via loss and parameter stability to stop training promptly and conserve compute.

4) Decision layer

On top sits a reinforcement learning engine that adapts to evolving maritime conditions and historical outcomes. It interfaces with existing VTS and port management tools to recommend or automate actions, delivering real-time decision support while fitting into current workflows.

Security and governance by design

• Sybil resistance: identity-bound credentials and membership controls prevent fake node proliferation.
• Consensus integrity: diversified validator sets and PBFT-style guarantees mitigate takeover attempts.
• Model hardening: robust aggregators and outlier detection curb data/model poisoning.
• Privacy preservation: differential privacy, secure aggregation, and zero-knowledge proofs (ZKPs) reduce inference risks.

Smart contracts are modular: an access control contract delivers role-based, fine-grained permissions; a verification contract automates integrity checks and anomaly detection; a permission management contract supports dynamic, time-bound escalation and multi-party authorization in emergencies; and an audit contract provides immutable traceability.

Data sharing follows standardized APIs with end-to-end encryption, strong authentication, and mandatory logging. Both streaming and batch exchanges are supported, with adaptive selection based on network conditions and mission urgency. A decentralized governance process—backed by on-chain voting—manages policies, dispute resolution, data quality standards, and evolving privacy rules, ensuring no single authority monopolizes control.

Hybrid data management

The system implements a hybrid on/off-chain strategy: immutable proofs and transaction records on-chain; high-volume telemetry off-chain with cryptographic bindings. Periodic integrity checks and ZKPs let participants validate data correctness without exposing contents. This approach minimizes storage costs, accelerates access, and maintains strong assurances of authenticity and non-repudiation.

Federated reinforcement learning for collaborative control

The FRL algorithm uses a multi-agent framework in which each authority runs a local agent tailored to its environment while contributing to a global policy. The state space captures:

• Vessel kinematics and trajectories
• Weather and environmental factors
• Traffic density and flow patterns
• Regulatory compliance states
• Inter-agency communication and coordination signals

The action space spans both direct interventions and cooperative maneuvers:

• Routing advisories, speed guidance, port and berth allocations
• Joint operations (e.g., coordinated search and rescue)
• Information-sharing protocols and escalation paths

A multi-objective reward balances safety, efficiency, and coordination, with penalties for regulatory violations; weights adjust dynamically to scenario priorities (e.g., severe weather vs. congestion management). Locally, agents update policies using standard RL methods (Q-learning or actor-critic). Model updates are shared via secure multi-party computation with differential privacy noise scheduled across training rounds to preserve utility while honoring a privacy budget.

To ensure reliability under adversarial and noisy conditions, the system:

• Applies median/trimmed-mean aggregation to ignore updates deviating beyond a configurable standard deviation threshold.
• Adapts noise variance over time to stabilize convergence without weakening privacy guarantees.
• Uses convergence tests on global loss and parameter drift to terminate training when stable.

Performance, scalability, and resilience

Adaptive resource allocation and load balancing optimize throughput across compute and network constraints. Consensus parameters can be tuned to traffic conditions, maintaining low latency for time-critical operations while batch analytics run off-peak. The horizontally scalable design supports the addition of new authorities without bottlenecks, and the distributed setup eliminates single points of failure. Even under node outages or malicious behavior, consensus validation, cryptographic safeguards, and robust learning keep the system operational.

Why it matters

This architecture delivers a rare combination: strong privacy and sovereignty, verifiable trust, and data-driven intelligence at global scale. For port operators, coast guards, and maritime regulators, it means faster incident response, smoother traffic flow, and safer seas—without centralizing sensitive data. By aligning blockchain’s auditability with federated RL’s collaborative optimization, the system sets a practical blueprint for next-generation maritime supervision networks.