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Reachability analysis of deep neural networks with provable guarantees
Ruan, W; Huang, X; Kwiatkowska, M (IJCAI, 19 July 2018)Verifying correctness of deep neural networks (DNNs) is challenging. We study a generic reachability problem for feed-forward DNNs which, for a given set of inputs to the network and a Lipschitz-continuous function over ... -
Scalable Orchestration of Service Function Chains in NFV-Enabled Networks: A Federated Reinforcement Learning Approach
Huang, H; Zeng, C; Zhao, Y; et al. (Institute of Electrical and Electronics Engineers (IEEE), 29 June 2021)Network function virtualization (NFV) is critical to the scalability and flexibility of various network services in the form of service function chains (SFCs), which refer to a set of Virtual Network Functions (VNFs) chained ... -
Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computing
Mills, J; Hu, J; Min, G (Institute of Electrical and Electronics Engineers (IEEE), 21 July 2021)Federated Learning (FL) is an emerging approach for collaboratively training Deep Neural Networks (DNNs) on mobile devices, without private user data leaving the devices. Previous works have shown that non-Independent and ...