Tarokh, VahidDiao, Enmao2023-10-032023-10-032023https://hdl.handle.net/10161/29196<p>In recent years, there has been a significant expansion in the scale and complexity of neural networks. This has resulted in significant demand for data, computation, and energy resources. In this light, it is crucial to enhance and optimize the efficiency of these ML models and algorithms. Additionally, the rise in computational capabilities of modern devices has prompted a shift towards distributed systems that enable localized data storage and model training. While this evolution promises substantial potential, it introduces a series of challenges. Such challenges encompass addressing the heterogeneity across systems, data, models, and supervision, balancing the trade-off among communication, computation, and performance, as well as building a community of shared interest to encourage collaboration in the emerging era of Artificial General Intelligence (AGI). In this dissertation, we contribute to the establishment of a theoretically justified, methodologically comprehensive, and universally applicable Efficient and Collaborative Distributed Machine Learning framework. Specifically, in Part I, we contribute to methodologies for Efficient Machine Learning including for both learning and inference. In this direction, we propose a parameter-efficient model, namely Restricted Recurrent Neural Networks (RRNN), that leverage the recurrent structures of RNNs using weight sharing in order to improve learning efficiency. We also introduce an optimal measure of vector sparsity named the PQ Index (PQI), and postulate a hypothesis connecting this sparsity measure and compressibility of neural networks. Based on this, we propose a Sparsity-informed Adaptive Pruning (SAP) algorithm. This algorithm adaptively determines the pruning ratio to enhance inference efficiency. In Part II, we address both efficiency and collaboration in Distributed Machine Learning. We introduce Distributed Recurrent Autoencoders for Scalable Image Compression (DRASIC), a data-driven Distributed Source Coding framework that can compress heterogeneous data in a scalable and distributed manner. We then propose Heterogeneous Federated Learning (HeteroFL), demonstrating the feasibility of training localized heterogeneous models to create a global inference model. Subsequently, we propose a new Federated Learning (FL) framework, namely SemiFL, to tackle Semi-Supervised Federated Learning (SSFL) for clients with unlabeled data. This method performs comparably with state-of-the-art centralized Semi-Supervised Learning (SSL), and fully supervised FL techniques. Finally, we propose Gradient Assisted Learning (GAL) in order to enable collaborations among multiple organizations without sharing data, models, and objective functions. This method significantly outperforms local learning baselines and achieves near-oracle performance. In Part III, we develop collaborative applications for building a community of shared interest. We apply SemiFL to Keyword Spotting (KWS), a technique widely used in virtual assistants. Numerical experiments demonstrate that one can train models from the scratch, or transfer from pre-trained models in order to leverage heterogeneous unlabeled on-device data, using only a small amount of labeled data from the server. Finally, we propose a Decentralized Multi-Target Cross-Domain Recommendation (DMTCDR) which enhances the recommendation performance of decentralized organizations without compromising data privacy or model confidentiality.</p>Electrical engineeringComputer scienceCollaborative Machine LearningDistributed Machine LearningEfficient Machine LearningSignal processingDissertation