8 papers · 1 filter
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…
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…
FDR-SVM: A Federated Distributionally Robust Support Vector Machine via a Mixture of Wasserstein Balls Ambiguity Set
Michael Ibrahim, Heraldo Rozas, Nagi Gebraeel +1
We study a federated classification problem over a network of multiple clients and a central server, in which each client's local data remains private and is subject to uncertainty…