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Found 7 papers, 3 papers with code
Privacy-Preserving Language Model Inference with Instance Obfuscation
Language Models as a Service (LMaaS) offers convenient access for developers and researchers to perform inference using pre-trained language models.
Labeling without Seeing? Blind Annotation for Privacy-Preserving Entity Resolution
We propose a novel blind annotation protocol based on homomorphic encryption that allows domain oracles to collaboratively label ground truths without sharing data in plaintext with other parties.
FedML-HE: An Efficient Homomorphic-Encryption-Based Privacy-Preserving Federated Learning System
Federated Learning trains machine learning models on distributed devices by aggregating local model updates instead of local data.
Secure & Private Federated Neuroimaging
Each site trains the neural network over its private data for some time, then shares the neural network parameters (i. e., weights, gradients) with a Federation Controller, which in turn aggregates the local models, sends the resulting community model back to each site, and the process repeats.
FedGCN: Convergence-Communication Tradeoffs in Federated Training of Graph Convolutional Networks
Methods for training models on graphs distributed across multiple clients have recently grown in popularity, due to the size of these graphs as well as regulations on keeping data where it is generated.
Secure Neuroimaging Analysis using Federated Learning with Homomorphic Encryption
Federated learning (FL) enables distributed computation of machine learning models over various disparate, remote data sources, without requiring to transfer any individual data to a centralized location.
A Concurrency-Optimal List-Based Set
Designing an efficient concurrent data structure is an important challenge that is not easy to meet.
Distributed, Parallel, and Cluster Computing
