Abstract Large-scale unlabeled data has allowed recent progress in self-supervised learning methods that learn rich visual representations. State-of-the-art self-supervised methods for learning representations from images (MoCo and BYOL) use an inductive bias that different augmentations (e.g. random crops) of an image should produce similar embeddings. We show that such methods are vulnerable to backdoor attacks where an attacker poisons a part of the unlabeled data by adding a small trigger (known to the attacker) to the images. The model performance […]

federated federated is the source code for the Bachelor’s Thesis. Privacy-Preserving Federated Learning Applied to Decentralized Data (Spring 2021, NTNU) Federated learning (also known as collaborative learning) is a machine learning technique that trains an algorithm across multiple decentralized edge devices or servers holding local data samples, without exchanging them. In this project, the decentralized data is the MIT-BIH Arrhythmia Database. Features ML pipelines using centralized learning or federated learning. Support for the following aggregation methods: Federated Stochastic Gradient Descent […]