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Found 6 papers, 2 papers with code
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments
In particular, Pelta constitutes the first attempt at defending an ensemble model against the Self-Attention Gradient attack to the best of our knowledge.
Pelta: Shielding Transformers to Mitigate Evasion Attacks in Federated Learning
The main premise of federated learning is that machine learning model updates are computed locally, in particular to preserve user data privacy, as those never leave the perimeter of their device.
Understanding Cryptocoins Trends Correlations
Secure encryption techniques guarantee the security of the transactions (transfers of coins across owners), registered into the ledger.
Shielding Federated Learning Systems against Inference Attacks with ARM TrustZone
Specifically, in FL, models are trained on the users devices and only model updates (i. e., gradients) are sent to a central server for aggregation purposes.
Plinius: Secure and Persistent Machine Learning Model Training
With the increasing popularity of cloud based machine learning (ML) techniques there comes a need for privacy and integrity guarantees for ML data.
TEEMon: A continuous performance monitoring framework for TEEs
Currently, only a limited number of performance measurement tools for TEE-based applications exist and none offer performance monitoring and analysis during runtime.
Cryptography and Security Distributed, Parallel, and Cluster Computing Performance C.4
