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Found 5 papers, 1 papers with code
Client-specific Property Inference against Secure Aggregation in Federated Learning
We formulate an optimization problem across different rounds in order to infer a tested property of every client from the output of the linear models, for example, whether they have a specific sample in their training data (membership inference) or whether they misbehave and attempt to degrade the performance of the common model by poisoning attacks.
Industry-Scale Orchestrated Federated Learning for Drug Discovery
To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n{\deg}831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups.
Constrained Differentially Private Federated Learning for Low-bandwidth Devices
This bandwidth and corresponding processing costs could be prohibitive if the participating entities are, for example, mobile devices.
Compression Boosts Differentially Private Federated Learning
In this paper, compressive sensing is used to reduce the model size and hence increase model quality without sacrificing privacy.
Federated Learning in Adversarial Settings
This paper presents a new federated learning scheme that provides different trade-offs between robustness, privacy, bandwidth efficiency, and model accuracy.
