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12/04/2022
Security Analysis of SplitFed Learning
Split Learning (SL) and Federated Learning (FL) are two prominent distri...
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11/01/2022
The Perils of Learning From Unlabeled Data: Backdoor Attacks on Semi-supervised Learning
Semi-supervised machine learning (SSL) is gaining popularity as it reduc...
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10/04/2022
Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
All state-of-the-art (SOTA) differentially private machine learning (DP ...
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10/15/2021
Mitigating Membership Inference Attacks by Self-Distillation Through a Novel Ensemble Architecture
Membership inference attacks are a key measure to evaluate privacy leaka...
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10/08/2021
FSL: Federated Supermask Learning
Federated learning (FL) allows multiple clients with (private) data to c...
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08/23/2021
Back to the Drawing Board: A Critical Evaluation of Poisoning Attacks on Federated Learning
While recent works have indicated that federated learning (FL) is vulner...
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10/02/2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning
Embedded systems demand on-device processing of data using Neural Networ...
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10/02/2020
Quantifying Privacy Leakage in Graph Embedding
Graph embeddings have been proposed to map graph data to low dimensional...
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12/24/2019
Cronus: Robust and Heterogeneous Collaborative Learning with Black-Box Knowledge Transfer
Collaborative (federated) learning enables multiple parties to train a m...
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06/15/2019
