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Found 9 papers, 0 papers with code
Investigating the Adversarial Robustness of Density Estimation Using the Probability Flow ODE
Beyond their impressive sampling capabilities, score-based diffusion models offer a powerful analysis tool in the form of unbiased density estimation of a query sample under the training data distribution.
Resource-Efficient Federated Hyperdimensional Computing
In conventional federated hyperdimensional computing (HDC), training larger models usually results in higher predictive performance but also requires more computational, communication, and energy resources.
Multi-Task Model Personalization for Federated Supervised SVM in Heterogeneous Networks
The proposed method utilizes efficient computations and model exchange in a network of heterogeneous nodes and allows personalization of the learning model in the presence of non-i. i. d.
Streaming Encoding Algorithms for Scalable Hyperdimensional Computing
Hyperdimensional computing (HDC) is a paradigm for data representation and learning originating in computational neuroscience.
Dynamic Network-Assisted D2D-Aided Coded Distributed Learning
Today, various machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of a wireless network.
Diverse Client Selection for Federated Learning via Submodular Maximization
In every communication round of federated learning, a random subset of clients communicate their model updates back to the server which then aggregates them all.
Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks
For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays.
Coded Computing for Federated Learning at the Edge
Federated Learning (FL) is an exciting new paradigm that enables training a global model from data generated locally at the client nodes, without moving client data to a centralized server.
Coded Federated Learning
Here, model parameters are computed locally by each client device and exchanged with a central server, which aggregates the local models for a global view, without requiring sharing of training data.
