Federated learning (FL) is a machine learning paradigm that aims to learn collaboratively from decentralized private data, owned by entities referred to as clients. In real-world applications of FL it is important to score the contribution of each client. The goal of this seminar is to provide a high-level overview of existing contribution-scoring techniques in federated learning using [1-2] and other references. 

Voraussetzungen

Federated learning is a machine learning paradigm where decentralized entities (clients) collaboratively learn using their private data. A central server acts as a coordinator of the learning process. Due to the sensitivity of the private data involved,  the data cannot be transferred. A salient problem of federated learning is the presence of malicious clients, which are clients that try to destroy the learning process. Malicious clients can do this by corrupting their data and/or by modifying their local model updates. The goal of this project is to understand how model poisoning attacks and defense strategies perform under different scenarios of federated learning using experiments. 

References: 

[1]- https://www.ndss-symposium.org/wp-content/uploads/ndss2021_6C-3_24498_paper.pdf

[2]- https://arxiv.org/pdf/1903.03936.pdf

[3]- https://arxiv.org/pdf/2304.00160.pdf

Voraussetzungen