We all hope for the best but sometimes, one must plan for ways of dealing with the worst-case scenarios, especially in a network with adversaries. This dissertation illustrates a detailed description of distributed optimization algorithms over a network of agents, in which some agents are adversarial and cause havoc. The model considered is such that adversarial agents act to subvert the objective of the network. The algorithms presented in this dissertation are solved via
gradient-based distributed optimization algorithm and the effects of the adversarial agents on the convergence of the algorithm to the optimal solution are also shown.
The analyses presented establish conditions under which the adversarial agents have enough information to obstruct convergence to the optimal solution by the non-adversarial agents. The adversarial agents act by using up network bandwidth, forcing the communication of the non-adversarial agents to be constrained. A distributed gradient-based optimization algorithm is explored in which the non-adversarial agents exchange quantized information with one another. Additionally, convergence of the solution to a neighborhood of the optimal solution is proved in the communication-constrained environment amidst the presence of adversarial agents.
Major: Electrical Engineering
Educational Career:
Bachelor's of Mathematics and Physics, BS, 2016, Lincoln University of Pennsylvania
Master's of Mathematics, MS, 2018, Delaware State University
Committee in Charge:
Chinwendu Enyioha, Chair, Electrical and Computer Engineering
George Atia, Electrical and Computer Engineering
Nazanin Rahnavard, Electrical and Computer Engineering
Zhihua Qu, Electrical and Computer Engineering
Adan Vela, Industrial Engineering and Management Systems
Approved for distribution by Chinwendu Enyioha, Committee Chair, on February 27, 2023.
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