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Found 9 papers, 2 papers with code
A Cautionary Tale: On the Role of Reference Data in Empirical Privacy Defenses
Within the realm of privacy-preserving machine learning, empirical privacy defenses have been proposed as a solution to achieve satisfactory levels of training data privacy without a significant drop in model utility.
ChatGPT for Shaping the Future of Dentistry: The Potential of Multi-Modal Large Language Model
Overall, LLMs have the potential to revolutionize dental diagnosis and treatment, which indicates a promising avenue for clinical application and research in dentistry.
Challenges and perspectives in computational deconvolution of genomics data
Deciphering cell type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases.
Local Model Reconstruction Attacks in Federated Learning and their Uses
Additionally, we propose a novel model-based attribute inference attack in federated learning leveraging the local model reconstruction attack.
Efficient passive membership inference attack in federated learning
In cross-device federated learning (FL) setting, clients such as mobiles cooperate with the server to train a global machine learning model, while maintaining their data locally.
Cell types and ontologies of the Human Cell Atlas
Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge.
Throughput-Optimal Topology Design for Cross-Silo Federated Learning
Federated learning usually employs a client-server architecture where an orchestrator iteratively aggregates model updates from remote clients and pushes them back a refined model.
Dynamic backup workers for parallel machine learning
This paradigm consists of $n$ workers, which iteratively compute updates of the model parameters, and a stateful PS, which waits and aggregates all updates to generate a new estimate of model parameters and sends it back to the workers for a new iteration.
Decentralized gradient methods: does topology matter?
Consensus-based distributed optimization methods have recently been advocated as alternatives to parameter server and ring all-reduce paradigms for large scale training of machine learning models.
