A5064761620- Stochastic Gradient Optimization Techniques
- Cryptography and Data Security
- Privacy-Preserving Technologies in Data
- Topic Modeling
- Recommender Systems and Techniques
ETH Zurich
2025
Graz University of Technology
2021-2022
Data science workflows are largely exploratory, dealing with under-specified objectives, open-ended problems, and unknown business value. Therefore, little investment is made in systematic acquisition, integration, pre-processing of data. This lack infrastructure results redundant manual effort computation. Furthermore, central data consolidation not always technically or economically desirable even feasible (e.g., due to privacy, and/or ownership). The ExDRa system aims provide for this...
Federated learning allows training machine (ML) models without central consolidation of the raw data. Variants such federated systems enable privacy-preserving ML, and address data ownership and/or sharing constraints. However, existing work mostly adopt data-parallel parameter-server architectures for mini-batch training, require manual construction runtime plans, largely ignore broad variety preparation, ML algorithms, model debugging. Over last years, we extended Apache SystemDS by an...