Privacy of Federated Machine Learning
Security of Machine Learning
Cryptography and Multi-Party Computation
Trusted Execution and Hardware Acceleration
Open Source, Open Data, and Applications
Privacy of Federated Machine Learning
- Federated Learning (survey)
- Robust Knowledge Transfer for Federated Learning
- Group Knowledge Transfer: Federated Learning of Large convolutional neural networks (CNNs) at the Edge
- Federated Multi-Tasking-Learning
Security of Machine Learning
- A Taxonomy of Attacks on Federated Learning
- Intellectual Property (IP) Protection / Model Stealing
- Poisoning Defences for Federated Learning: Goals, Challenges and Solution Approaches
Cryptography and Multi-Party Computation
- MP2ML: A Mixed-Protocol Machine Learning Framework for Private Inference
- A Scalable Approach for Privacy-Preserving Collaborative Machine Learning
Trusted Execution and Hardware Acceleration
- State of the Art of TEE Architectures and Applications to Machine Learning
- Machine Learning on Encrypted Data: Hardware-Software Codesign
- Role of Trusted Execution Environments in PPML
Open Source, Open Data, and Applications
- Open Source Frameworks and Plans for Federated Machine Learning
- Machine Learning applied to malware detection/classification and its extent to Federated Learning
- FedML: A Research Library and Benchmark for Federated Machine Learning
- Cyber-Risk Intelligence Sharing using Federated Learning
