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Showing 1 to 7 of 7 for “"Split Learning"”.
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Split learning on FPGAs
… At the same time, the field of machine learning is diversifying to include distributed deep learning methods like split learning, which can help preserve privacy by avoiding the sharing of raw data and model details. In order to continue to expand the capabilities of architectures like …
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Computational Privacy with Split Learning: Benchmarking of Algorithmic Defenses against Reconstruction Attacks
Distributed deep learning has potential for significant impact in preserving data privacy and improving model accuracy by leveraging massive sets of training data. However, passing intermediate weights, gradients, or activations is inherent in current distributed learning techniques, all of which …
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Distributed Deep Learning in IoT: Splitting Neural Networks in Inference and Training
… Knowledge Distillation via Collaboratively Learning (KDCL) to Split Learning to get a distilled model of server-part model in SplitNN after training. The distilled model could be designed to fit the memory-limited Internet of Things (IoT) devices so that clients could get prediction results …
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Distributed Machine Learning in Heterogeneous Edge Networks
… edge. Meanwhile, the complexity of machine learning models has increased significantly, with state-of-the-art models for tasks like natural language processing and computer vision now containing billions of parameters.Distributed machine learning addresses the challenges posed by massive …
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Decision Making for Populations
… large heterogeneous populations by privately learning from decentralized data sources. First, we introduce DeepABM a framework for Scalable, Fast and Differentiable Agent-based Modeling. DeepABM can simulate million-size populations in a few seconds on personal computers (up to 300x faster …
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Towards Intelligent Federated Learning Systems
… the privacy challenges of traditional machine learning have become more visible. Recent works leverage edge computing to preserve data privacy by keeping the data where it is (not shared during the training process), so-called "Edge Computing". In 2016, Google extended this idea to distributed …
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AI-Enabled and Integrated Sensing-Based Beam Management Strategies in Open RAN
… use of Artificial Intelligence (AI) and Machine Learning (ML)-based solutions. Moreover, beam management represents some fundamental use cases defined by Open RAN Alliance (O-RAN). This work analyses beam management strategies in Open RAN and proposes solutions for codebook-based mmWave systems …