Personalized Support Vector Machines for Privacy-Preserving Federated Learning
Abstract
The pervasive digitalization of the environment enables monitoring of real-world systems by collecting data from multiple diverse sources. Such data may correspond to problems such as network intrusion detection or predicting the state of the wireless channel. While machine learning (ML)-based solutions are increasingly employed in such tasks, centralized learning approaches often fall short in terms of scalability and privacy. A more practical alternative is offered by federated learning (FL), which enables collaborative model training without disclosing raw data. However, FL brings a new set of challenges such as heterogeneous data distributions, diverse computational resources, and privacy requirements, which are rarely considered together in the algorithm design. This dissertation addresses the heterogeneity of the data structure and distribution, the heterogeneity of the computational resources, and the requirements for privacy preservation. Motivated by the fact that such factors may be present simultaneously, this dissertation focuses on developing lightweight privacy-preserving personalized FL algorithms. The work aims to bridge the gap in the FL field by formulating a set of algorithms for learning classification/regression models (i), proposing policies for operating in highly heterogeneous systems and assessing their applicability in different scenarios (ii), assessing the applicability of conventional privacy preservation measures (iii), and proposing novel…
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