Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
arXiv:2602.13485v2 Announce Type: replace Abstract: Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series data. These subsystems are often interdependent, making it important to understand how temporal patterns at one subsystem relate to others. This is challenging in decentralized settings where raw measurements cannot be shared and client observations are heterogeneous. In practical deployments each subsystem (client) operates a fixed proprietary model that cannot be mod
This paper addresses critical challenges in federated learning for industrial systems, which is becoming increasingly relevant given the proliferation of IoT sensors and the need for data privacy and localized processing.
It introduces a method for understanding complex temporal interdependencies in decentralized industrial systems without sharing raw data, enabling more sophisticated AI deployment in sensitive environments.
The ability to model nonlinear temporal dynamics and interpret cross-client relationships in federated learning will improve the security, efficiency, and reliability of distributed industrial AI applications.
- · Industrial IoT providers
- · Smart manufacturing companies
- · Edge AI developers
- · Critical infrastructure operators
- · Centralized data processing models
- · Systems highly reliant on raw data sharing
Improved predictive maintenance and anomaly detection in distributed industrial systems.
Increased adoption of federated learning in sectors with strict data privacy or regulatory requirements.
New AI-powered services emerging from insights derived from previously inaccessible cross-system data.
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Read at arXiv cs.LG