Peng Wu PhD Dissertation Defense

Announcing:
PhD Dissertation Defense

Name:
Peng Wu

Title:
Bayesian Data Fusion for Distributed Learning

Date:
4/12/2024

Time:
12:30:00 PM

Location:
ISEC 532

Committee Members:
Prof. Pau Closas (Advisor)
Prof. Deniz Erdogmus
Prof. Lili Su

Abstract:
The necessity for distributed data fusion arises from the increasing demand to integrate diverse and voluminous data sources, especially in applications where large numbers of users are collaborating to perform inference and learning tasks. This integration is crucial when data is available in a distributed manner or originates from various sensor types, aiming to deduce specific quantities of interest accurately. Moreover, the importance of privacy cannot be overstated, particularly in scenarios where sensitive information, such as location data, is involved. Federated learning emerges as a pivotal solution in this context, enabling model training on local datasets without the need to exchange the data itself, thus preserving user privacy. However, the deployment of these technologies encounters significant challenges, including the multiple counting problem in data fusion, where data may be redundantly used across different estimations without user awareness, and the non-IID problem in federated learning, where the non-identically distributed nature of data across clients can severely hamper the model’s performance.

To address these challenges, this dissertation explores the intersection of data fusion, federated learning, and Bayesian methods, with a focus on applied problems in indoor localization, satellite-based navigation, and image processing that spans both theoretical analysis and practical application. In the realm of data fusion, we delve into the Bayesian framework to offer a solution that not only facilitates the optimal integration of sensor data with prior knowledge but also navigates the intricacies of feature fusion effectively. This approach mitigates the multiple counting issue by ensuring that the fusion of local estimates accounts for the overuse of prior knowledge. In tackling the problems inherent to federated learning, particularly the non-IID issue, we introduce novel frameworks and algorithms designed to enhance model training and performance in a privacy-preserving manner. We explore personalized and clustered federated learning as methods to customize the learning process to individual client characteristics and to group clients with similar data traits, respectively. A number of practical problems are explored using those federated methodologies, including indoor fingerprinting, jamming interference classification, or image classification tasks. Noticeably, this thesis proposes a novel Bayesian clustered federated learning framework that generalizes existing clustered federated learning schemes by leveraging Bayesian data association modeling. By implementing a Bayesian perspective within these frameworks, the dissertation proposes practical algorithms that achieve a balance between performance and computational efficiency, ultimately advancing the application of distributed data fusion and federated learning in privacy-sensitive fields.