M.Sc. Marvin Xhemrishi
Technical University of Munich
Associate Professorship of Coding and Cryptography (Prof. Wachter-Zeh)
Postal address
Postal:
Theresienstr. 90
80333 München
Theses in Progress
Privacy-Preserving Vertical Federated XGBoost on GPU
Description
Gradient boosting tree model is widely used in practical applications, and its optimized implementation XGBoost is one of the most popular machine learning algorithms. If the data is centralized, It has been well implemented. When the data is decentralized and to preserve privacy, federated learning (FL) is used. FL is widely divided into ‘horizontal FL’ and ‘vertical FL,’ which differ from the partition methods of datasets. Previous works have focused more on horizontal FL, while vertical FL remains unexplored. Existing works in vertical federated XGBoost have some limitations, such as intermediate data leakage, computation overhead, etc. GPU acceleration is quite common in machine learning and is not applied to privacy-preserving XGBoost. This project aims to build an efficient privacy-preserving vertical federated XGBoost using GPUs.
Supervisor:
SwiftAgg Federated Learning
Description
Federated learning is a promising learning method for data that is decentralized. For privacy reasons, the devices that own the data cannot share it without performing extra computations (such as encryption, secret sharing etc). Since the end devices have to compute local gradients over their dataset, then the presence of low processing nodes, referred to as stragglers, can outweigh the benefits of the parallelism. A central entity serves as an aggregation node, that aggregates the local gradients received from the end devices. Due to privacy reasons, the aggregation node shall not learn the dataset of a device from the local gradient it computes. SwiftAgg[1] is a scheme that allows straggler resiliency and secure aggregation.
[1] - https://arxiv.org/pdf/2202.04169.pdf
Prerequisites
- Information Theory
Contact
marvin.xhemrishi@tum.de
Supervisor:
2022
- Adaptive private distributed matrix multiplication. IEEE Transactions on Information Theory, 2022 more…
- The Wiretap Channel for Capacitive PUF-Based Security Enclosures. IACR Transactions on Cryptographic Hardware and Embedded Systems, 2022 more…
- Analysis of Communication Channels Related to Physical Unclonable Functions. Workshop on Coding and Cryptography (WCC), 2022 more…
- Distributed Linear Computations over Private Sparse Matrices. IEEE European School of Information Theory, 2022 more…
- Distributed Linear Computations over Private Sparse Matrices. Munich Workshop on Coding and Cryptography, 2022 more…
- Computational Code-Based Privacy for Coded Federated Learning. TUM ICE Workshop Raitenhaslach, 2022 more…
- Distributed Matrix-Vector Multiplication with Sparsity and Privacy Guarantees. IEEE International Symposium on Information Theory, 2022 more…
- Efficient Private Storage of Sparse Machine Learning Data. IEEE Information Theory Workshop, 2022 more…
- Computational Code-Based Privacy in Coded Federated Learning. IEEE International Symposium on Information Theory, 2022 more…
2021
2020
2019
- List Decoding of Short Codes for Communication over Unknown Fading Channels. , Workshop on Coding, Cooperation, and Security in Modern Communication Networks (COCO 2019) . , 2019 more…
- List Decoding for Fading Channels. Oberpfaffenhofen Workshop on High Throughput Coding (OWHTC) , 2019 more…
- List Decoding of Short Codes for Communication over Unknown Fading Channels. Asilomar Conference on Signals, Systems, and Computers, 2019 more…