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Found 6 papers, 2 papers with code
Federated Learning for Semantic Parsing: Task Formulation, Evaluation Setup, New Algorithms
By leveraging data from multiple clients, the FL paradigm can be especially beneficial for clients that have little training data to develop a data-hungry neural semantic parser on their own.
Joint Coreset Construction and Quantization for Distributed Machine Learning
Coresets are small, weighted summaries of larger datasets, aiming at providing provable error bounds for machine learning (ML) tasks while significantly reducing the communication and computation costs.
Communication-efficient k-Means for Edge-based Machine Learning
We consider the problem of computing the k-means centers for a large high-dimensional dataset in the context of edge-based machine learning, where data sources offload machine learning computation to nearby edge servers.
Sharing Models or Coresets: A Study based on Membership Inference Attack
Distributed machine learning generally aims at training a global model based on distributed data without collecting all the data to a centralized location, where two different approaches have been proposed: collecting and aggregating local models (federated learning) and collecting and training over representative data summaries (coreset).
Overcoming Noisy and Irrelevant Data in Federated Learning
A challenge is that among the large variety of data collected at each client, it is likely that only a subset is relevant for a learning task while the rest of data has a negative impact on model training.
Coupling Random Orthonormal Projection with Gaussian Generative Model for Non-Interactive Private Data Release
A key challenge facing the design of differential privacy in the non-interactive setting is to maintain the utility of the released data.
Cryptography and Security
