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Description
The presence of strategic agents can pose unique challenges to data collection and distributed learning. This dissertation first explores the social network dimension of data collection markets, and then focuses on how the strategic agents can be efficiently and effectively incentivized to cooperate in distributed machine learning frameworks.
The first problem explores the impact of social learning in collecting and trading unverifiable information where a data collector purchases data from users through a payment mechanism. Each user starts with a personal signal which represents the knowledge about the underlying state the data collector desires to learn. Through social interactions, each user also acquires additional information from his neighbors in the social network. It is revealed that both the data collector and the users can benefit from social learning which drives down the privacy costs and helps to improve the state estimation for a given total payment budget. In the second half, a federated learning scheme to train a global learning model with strategic agents, who are not bound to contribute their resources unconditionally, is considered. Since the agents are not obliged to provide their true stochastic gradient updates and the server is not capable of directly validating the authenticity of reported updates, the learning process may reach a noncooperative equilibrium. First, the actions of the agents are assumed to be binary: cooperative or defective. If the cooperative action is taken, the agent sends a privacy-preserved version of stochastic gradient signal. If the defective action is taken, the agent sends an arbitrary uninformative noise signal. Furthermore, this setup is extended into the scenarios with more general actions spaces where the quality of the stochastic gradient updates have a range of discrete levels. The proposed methodology evaluates each agent's stochastic gradient according to a reference gradient estimate which is constructed from the gradients provided by other agents, and rewards the agent based on that evaluation.
ContributorsAkbay, Abdullah Basar (Author) / Tepedelenlioğlu, Cihan (Thesis advisor) / Spanias, Andreas (Committee member) / Kosut, Oliver (Committee member) / Ewaisha, Ahmed (Committee member) / Arizona State University (Publisher)
Created2023
Description
We consider the problem of routing packets with end-to-end hard deadlines in multihop communication networks. This is a challenging problem due to the complex spatial-temporal correlation among flows with different deadlines especially when significant traffic fluctuation exists. To tackle this problem, based on the spatial-temporal routing algorithm that specifies where and when a packet should be routed using concepts of virtual links and virtual routes, we proposed a constrained resource-pooling heuristic into the spatial-temporal routing, which enhances the ``work-conserving" capability and improves the delivery ratio. Our extensive simulations show that the policies improve the performance of spatial-temporal routing algorithm and outperform traditional policies such as backpressure and earliest-deadline-first (EDF) for more general traffic flows in multihop communication networks.
ContributorsWang, Weichang (Author) / Ying, Lei (Thesis advisor) / Zhang, Junshan (Committee member) / Ewaisha, Ahmed (Committee member) / Arizona State University (Publisher)
Created2018