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Found 6 papers, 0 papers with code
Accelerating Transfer Learning with Near-Data Computation on Cloud Object Stores
This facilitates our second technique, storage-side batch adaptation, which enables increased concurrency in the storage tier while avoiding out-of-memory errors.
SecFL: Confidential Federated Learning using TEEs
Second, malicious clients can collude with each other to steal data, models from regular clients or corrupt the global training model.
Perun: Secure Multi-Stakeholder Machine Learning Framework with GPU Support
To address this problem, we designed and implemented Perun, a framework for confidential multi-stakeholder machine learning that allows users to make a trade-off between security and performance.
secureTF: A Secure TensorFlow Framework
To tackle this challenge, we designed secureTF, a distributed secure machine learning framework based on Tensorflow for the untrusted cloud infrastructure.
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
TensorSCONE: A Secure TensorFlow Framework using Intel SGX
This imposes significant security risks since modern online services rely on cloud computing to store and process the sensitive data.
