10 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…
Prognostics for Autonomous Deep-Space Habitat Health Management under Multiple Unknown Failure Modes
Benjamin Peters, Ayush Mohanty, Xiaolei Fang +2
Deep-space habitats (DSHs) are safety-critical systems that must operate autonomously for long periods, often beyond the reach of ground-based maintenance or expert intervention. M…
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…
Learning Unknown Interdependencies for Decentralized Root Cause Analysis in Nonlinear Dynamical Systems
Ayush Mohanty, Paritosh Ramanan, Nagi Gebraeel
Root cause analysis (RCA) in networked industrial systems, such as supply chains and power networks, is notoriously difficult due to unknown and dynamically evolving interdependenc…
A Federated Generalized Expectation-Maximization Algorithm for Mixture Models with an Unknown Number of Components
Michael Ibrahim, Nagi Gebraeel, Weijun Xie
We study the problem of federated clustering when the total number of clusters across clients is unknown, and the clients have heterogeneous but potentially overlapping cluster…