Theses and Dissertations


Issuing Body

Mississippi State University


Qian, Du

Committee Member

Bo, Tang

Committee Member

Fu, Yong

Committee Member

Ball, John

Date of Degree


Document Type

Dissertation - Open Access


Electrical and Computer Engineering

Degree Name

Doctor of Philosophy (Ph.D)


James Worth Bagley College of Engineering


Department of Electrical and Computer Engineering


In the past 10 years, the growth of machine learning technology has been significant, largely due to the availability of large datasets for training. However, gathering a sufficient amount of data on a central server can be challenging. Additionally, with the rise of mobile networking and the large amounts of data generated by IoT devices, privacy and security issues have become a concern, resulting in government regulations such as GDPR, HIPAA, CCPA, and ADPPA. Under these circumstances, traditional centralized machine learning methods face a problem in that sensitive data must be kept locally for privacy reasons, making it difficult to achieve the desired learning outcomes. Federated learning (FL) offers a solution to this by allowing for a global shared model to be trained by exchanging locally computed optimums instead of sharing the actual data.

Despite its success as a natural solution for IoT machine learning implementation, Federated learning (FL) still faces challenges with regards to security and performance. These include high communication costs between IoT devices and the central server, the potential for sensitive information leakage and reduced model precision due to the aggregation process in the distributed IoT network, and performance concerns caused by the heterogeneity of data and devices in the network.

In this dissertation, I present practical and effective techniques with strong theoretical supports to address these challenges. To optimize communication resources, I introduce a new multi-server FL framework called MS-FedAvg. To enhance security, I propose a robust defense algorithm called LoMar. To address data heterogeneity, I present FedLGA, and for device heterogeneity, I propose FedSAM.