Robust M-Estimation Based Distributed Expectation Maximization Algorithm with Robust Aggregation

Distributed networks are widely used in industrial and consumer applications. As the communication capabilities of such networks are usually limited, it is important to develop algorithms which are capable of handling the vast amount of data processing locally and only communicate some aggregated va...

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Bibliographic Details
Published in:Proceedings of the ... IEEE International Conference on Acoustics, Speech and Signal Processing (1998) pp. 1 - 5
Main Authors: Schroth, Christian A., Vlaski, Stefan, Zoubir, Abdelhak M.
Format: Conference Proceeding
Language:English
Published: IEEE 04.06.2023
Subjects:
Acoustics
Adaptation models
biweight estimator
Data aggregation
Data processing
decentralized
distributed
EM algorithm
M-estimation
malicious agents
robust
robust aggregation
Robustness
Signal processing
Signal processing algorithms
Tukey
ISSN:2379-190X
Online Access:Get full text
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Summary:Distributed networks are widely used in industrial and consumer applications. As the communication capabilities of such networks are usually limited, it is important to develop algorithms which are capable of handling the vast amount of data processing locally and only communicate some aggregated value. Additionally, these algorithms have to be robust against outliers in the data, as well as faulty or malicious nodes. Thus, we propose a robust distributed expectation maximization (EM) algorithm based on Real Elliptically Symmetric (RES) distributions, which is highly adaptive to outliers and moreover is combined with a robust data aggregation step which provides robustness against malicious nodes. In the simulations, the proposed algorithm shows its effectiveness over non-robust methods.
ISSN:2379-190X
DOI:10.1109/ICASSP49357.2023.10096191