4 papers
Federated Learning of Nonlinear Temporal Dynamics with Graph Attention-based Cross-Client Interpretability
Ayse Tursucular, Ayush Mohanty, Nazal Mohamed +1
Networks of modern industrial systems are increasingly monitored by distributed sensors, where each system comprises multiple subsystems generating high dimensional time series dat…
Towards Uncertainty-Aware Federated Granger Causal Learning
Ayush Mohanty, Nazal Mohamed, Nagi Gebraeel
Granger causality recovers directed interactions from time-series data, but in many distributed systems, the data are vertically partitioned across clients, with each client observ…
Federated Causal Representation Learning in State-Space Systems for Decentralized Counterfactual Reasoning
Nazal Mohamed, Ayush Mohanty, Nagi Gebraeel
Networks of interdependent industrial assets (clients) are tightly coupled through physical processes and control inputs, raising a key question: how would the output of one client…
Federated Granger Causality Learning for Interdependent Clients with State Space Representation
Ayush Mohanty, Nazal Mohamed, Paritosh Ramanan +1
Advanced sensors and IoT devices have improved the monitoring and control of complex industrial enterprises. They have also created an interdependent fabric of geographically distr…