Resilient Distributed Parameter Estimation With Heterogeneous Data

By Yuan Chen, Soummya Kar, José M. F. Moura. 2018

39 citations recorded by the source corpus.

This paper studies resilient distributed estimation under measurement attacks. A set of agents each makes successive local, linear, noisy measurements of an unknown vector field collected in a vector parameter. The local measurement models are heterogeneous across agents and may be locally unobservable for the unknown parameter. An adversary compromises some of the measurement streams and changes their values arbitrarily. The agents’ goal is to cooperate over a peer-to-peer communication network to process their (possibly compromised) local measurements and estimate the value of the unknown vector parameter. We present SAGE, the Saturating Adaptive Gain Estimator, a distributed, recursive, consensus + innovations estimator that is resilient to measurement attacks. We demonstrate that, as long as the number of compromised measurement streams is below a particular bound, then, SAGE guarantees that all of the agents’ local estimates converge almost surely to the value of the parameter. The resilience of the estimator – i.e., the number of compromised measurement streams it can tolerate – does not depend on the topology of the inter-agent communication network. Finally, we illustrate the performance of SAGE through numerical examples.

Subfields: Artificial Intelligence, Computer Networks and Communications

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Citations in the corpus

Robust Aggregation for Federated Learning (2019, background): “Robust estimation in networks was considered in [4, 24, 89].”