… with in-network deep learning (DL) … the functional split is applied so that the deep neural
network (DNN) is decomposed into sub-elements of the data plane for making machine learning …

… split optimization problems using a deep learning … the functional splits of the BSs to minimize
the total network cost in the vRAN system. First, we formulate and present the functional split …

… Distributed machine learning methods promise to mitigate these problems. We argue for a
split learning based approach and apply this distributed learning method for the first time in …

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep …
approaches based on split computing (SC) have been proposed, where the DNN is split into a …

… leads to some information leakage for the scenario of split manufacturing, where the structural
… We believe (and demonstrate) that deep learning (DL) is a good match for attacking split …

… Split Learning and Parallel Split learning, the attacker is able to easily reconstruct the original
data. To overcome this problem, we changed the loss function … 6]) to the loss function Cross …

… a challenging problem on how to design the functional split configuration. In this paper, a
deep reinforcement learning approach is proposed to optimize function splitting in vRAN. A …

… just a mathematical function mapping some set of input values to output values. The function
is … We can think of each application of a different mathematical function as providing a new …

… a deep neural network, J is a realvalued function representing a … applicability to standard
deep learning architectures. In our work, … c: Operator splitting methods Operator splitting, and in …

… For the global network, we use a basic FNN with one hidden layer with size 500, and use
the tanh function as the activation and the MSE as the loss function. For training the global …