# SeaCausal-FL: Federated Fuzzy Causal Learning for Maritime IoT Fault Diagnosis and Counterfactual Reasoning

arXiv: https://arxiv.org/abs/2609.06257 · HTML: https://mlsanity.com/paper/2609.06257/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 56 references that are in the map.
- First posted on arXiv 5 Sep 2026.

## Builds on (most cited first, 25 of 25)

- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Model-Contrastive Federated Learning](https://mlsanity.com/paper/2103.16257/) (2021)
- [Ditto: Fair and Robust Federated Learning Through Personalization](https://mlsanity.com/paper/2012.04221/) (2020)
- [FedBN: Federated Learning on Non-IID Features via Local Batch Normalization](https://mlsanity.com/paper/2102.07623/) (2021)
- [AdaptSFL: Adaptive Split Federated Learning in Resource-constrained Edge Networks](https://mlsanity.com/paper/2403.13101/) (2024)
- [SplitLoRA: A Split Parameter-Efficient Fine-Tuning Framework for Large Language Models](https://mlsanity.com/paper/2407.00952/) (2024)
- [FedSN: A Federated Learning Framework over Heterogeneous LEO Satellite Networks](https://mlsanity.com/paper/2311.01483/) (2023)
- [Hierarchical Split Federated Learning: Convergence Analysis and System Optimization](https://mlsanity.com/paper/2412.07197/) (2024)
- [SatFed: A Resource-Efficient LEO Satellite-Assisted Heterogeneous Federated Learning Framework](https://mlsanity.com/paper/2409.13503/) (2024)
- [IC3M: In-Car Multimodal Multi-object Monitoring for Abnormal Status of Both Driver and Passengers](https://mlsanity.com/paper/2410.02592/) (2024)
- [IFViT: Interpretable Fixed-Length Representation for Fingerprint Matching via Vision Transformer](https://mlsanity.com/paper/2404.08237/) (2024)
- [HASFL: Heterogeneity-aware Split Federated Learning over Edge Computing Systems](https://mlsanity.com/paper/2506.08426/) (2025)
- [Federated Causality Learning with Explainable Adaptive Optimization](https://mlsanity.com/paper/2312.05540/) (2023)
- [HFedMoE: Resource-aware Heterogeneous Federated Learning with Mixture-of-Experts](https://mlsanity.com/paper/2601.00583/) (2026)
- [LLM-Driven Stationarity-Aware Expert Demonstrations for Multi-Agent Reinforcement Learning in Mobile Systems](https://mlsanity.com/paper/2511.19368/) (2025)
- [RRTO: A High-Performance Transparent Offloading System for Model Inference in Mobile Edge Computing](https://mlsanity.com/paper/2507.21739/) (2025)
- [Optimizing Split Federated Learning with Unstable Client Participation](https://mlsanity.com/paper/2509.17398/) (2025)
- [NSC-SL: A Bandwidth-Aware Neural Subspace Compression for Communication-Efficient Split Learning](https://mlsanity.com/paper/2602.02696/) (2026)
- [Cellular Communications in Ocean Waves for Maritime Internet of Things](https://mlsanity.com/paper/2004.07417/) (2020)
- [Federated Meta-Learning for Few-Shot Fault Diagnosis with Representation Encoding](https://mlsanity.com/paper/2310.09002/) (2023)
- [A Survey on Data-Driven Fault Diagnostic Techniques for Marine Diesel Engines](https://mlsanity.com/paper/2404.10363/) (2024)
- [IRS Aided Federated Learning: Multiple Access and Fundamental Tradeoff](https://mlsanity.com/paper/2412.00422/) (2024)
- [Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning](https://mlsanity.com/paper/2602.19414/) (2026)
- [Marine Engine Fault Dataset: Open-Access Data under Controlled Reference and Fault Scenario Conditions](https://mlsanity.com/paper/2607.19444/) (2026)

## Abstract (arXiv)

Reliable marine-engine fault diagnosis in maritime IoT is challenged by distributed data ownership, heterogeneous fault distributions, and continuously changing operating conditions. This paper proposes SeaCausal-FL, a federated fuzzy causal learning framework that combines a shared temporal diagnostic path with mechanism-conditioned causal reasoning. An interval type-2 fuzzy layer represents uncertain and overlapping operating mechanisms, while each mechanism is associated with a physics-constrained structural causal model. Before aggregation, locally learned mechanisms are aligned using operating context, causal structure, and conditional intervention-response signatures. Model parameters are then aggregated according to sample, class, mechanism, and mechanism-class evidence instead of client sample size alone. The learned structural equations further support interval counterfactual reasoning through abduction, action, and prediction. Experiments on a marine-engine fault dataset and a real-data-calibrated semi-synthetic causal benchmark show that SeaCausal-FL achieves an average F1 score of 87.07% across four client partitions, with AUROC and AUPRC of 98.98% and 94.81%, respectively. It also maintains strong performance under unseen loads and fault-type omission during training. On the causal benchmark, SeaCausal-FL reaches an Edge-F1 of approximately 0.58 and an Edge-AUPRC of 0.68, reduces coefficient RMSE to about 0.14, and provides favorable counterfactual estimation and intervention decisions.