Distributed Learning with Enhanced Efficiency, Robustness and Privacy
Abstract
Distributed machine learning for Internet of Things (IoT) and cyber-physical systems (CPS) applications faces increasing demands for efficiency, privacy, and robustness. This thesis focuses on addressing these challenges through two primary approaches, i.e., fully-distributed learning algorithms and federated learning (FL) algorithms, which form the main contributions of the work. This study proposes machine learning algorithms for distributed and federated learning in the context of IoT and CPS applications. Developing distributed algorithms for artificial intelligence is necessary as centralized data processing may be unfeasible due to computational and communication costs and privacy concerns. This work aims to address challenges in fully-distributed learning and FL settings, focusing on resilience against attacks, robustness to communication noise, and privacy preservation. The main contributions of the thesis can be grouped into the following categories: resilience of partial-sharing-based online federated learning against model poisoning attacks noise-robust and resource-efficient federated learning privacy-preserving distributed nonnegative matrix factorization distributed maximum consensus with noisy communication links. In the context of federated learning, we analyze the resilience of the partial-sharing based online FL (PSO-Fed) algorithm to model-poisoning attacks. We show that PSO-Fed outperforms other communication-efficient FL algorithms against modelpoisoning…
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