<div>Petri net (PN) is a useful tool for the modeling and analysis of complex systems and has been widely used in a variety of practical systems. This dissertation aims at studying highway transportation systems using Petri nets and investigating several fundamental problems related to the modeling, state/structure estimation, and control of highway traffic.</div><div>This dissertation starts with two kinds of modeling schemes. The first one uses the Probabilistic Petri net to model a highway segment. The traffic movement probabilities have also been shown. The second scheme uses the traditional Petri net structure to model the traffic network around a city’s metropolitan area, where places represent the destinations of interests and tokens represent time units.</div><div>After that, two estimation algorithms and one control algorithm have been proposed, respectively, based on external observations. The first algorithm deals with labeled Petri nets and the objective is to estimate the minimum initial marking that has (have) the smallest token sum. The second algorithm estimates the Petri net structures from the observations of finite token change sequences in terms of the minimum number of transitions and connections. At last, the traffic volume control algorithm is to keep the traffic volume within capacity. The controller will be applied in each evolution step depending on observation.</div><div>Since we have been focusing on the optimization problems of the structure and markings of the Petri net, it is directly related to the optimal route planning problems in highway traffic scenarios. Thus, we can obtain optimized traveling routes by applying proposed algorithms to the traffic systems.</div>

Federated learning has gained attention recently for its ability to protect data privacy and distribute computing loads [1]. It overcomes the limitations of traditional machine learning algorithms by allowing computers to train on remote data inputs and build models while keeping participant privacy intact. Traditional machine learning offered a solution by enabling computers to learn patterns and make decisions from data without explicit programming. It opened up new possibilities for automating tasks, recognizing patterns, and making predictions. With the exponential growth of data and advances in computational power, machine learning has become a powerful tool in various domains, driving innovations in fields such as image recognition, natural language processing, autonomous vehicles, and personalized recommendations. traditional machine learning, data is usually transferred to a central server, raising concerns about privacy and security. Centralizing data exposes sensitive information, making it vulnerable to breaches or unauthorized access. Centralized machine learning assumes that all data is available at a central location, which is only sometimes practical or feasible. Some data may be distributed across different locations, owned by different entities, or subject to legal or privacy restrictions. Training a global model in traditional machine learning involves frequent communication between the central server and participating devices. This communication overhead can be substantial, particularly when dealing with large-scale datasets or resource-constrained devices. / Recent studies have uncovered security issues with most of the federated learning models. One common false assumption in the federated learning model is that participants are the attacker and would not use polluted data. This vulnerability enables attackers to train their models using polluted data and then send the polluted updates to the training server for aggregation, potentially poisoning the overall model. In such a setting, it is challenging for an edge server to thoroughly inspect the data used for model training and supervise any edge device. This study evaluates the vulnerabilities present in federated learning and explores various types of attacks that can occur. This paper presents a robust prevention scheme to address these vulnerabilities. The proposed prevention scheme enables federated learning servers to monitor participants actively in real-time and identify infected individuals by introducing an encrypted verification scheme. The paper outlines the protocol design of this prevention scheme and presents experimental results that demonstrate its effectiveness. / Thesis / Doctor of Philosophy (PhD) / federated learning models face significant security challenges and can be vulnerable to attacks. For instance, federated learning models assume participants are not attackers and will not manipulate the data. However, in reality, attackers can compromise the data of remote participants by inserting fake or altering existing data, which can result in polluted training results being sent to the server. For instance, if the sample data is an animal image, attackers can modify it to contaminate the training data. This paper introduces a robust preventive approach to counter data pollution attacks in real-time. It incorporates an encrypted verification scheme into the federated learning model, preventing poisoning attacks without the need for specific attack detection programming. The main contribution of this paper is a mechanism for detection and prevention that allows the training server to supervise real-time training and stop data modifications in each client's storage before and between training rounds. The training server can identify real-time modifications and remove infected remote participants with this scheme.