Decentralized Federated Learning

Description: With the increasing popularity of Internet of Things (IoT) devices, Decentralized Federated Learning (DFL) has emerged as a promising approach to AI that aims to address some of the limitations of traditional Federated Learning (FL) methods, such as the need for a centralized server. However, most of the existing frameworks to train FL models in a decentralized manner i) lack of interesting evaluation scenarios , ii) do not manage the federation resources in an optimun way, iii) do not detect and mitigate cyberattacks, and iv) do not consider the trustworthiness of AI models.

Therefore,the following table summarizes some open topics offered for masters and projects.

Fedstellar https://federatedlearning.inf.um.es/ 

Martinez Beltrán, E. T., et al. (2022). Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges. arXiv preprint arXiv:2211.08413. 

Bonawitz, K., et al. (2019). Towards federated learning at scale: System design. In Proceedings of the 2nd SysML Conference (pp. 1-10).

Kairouz, P., et al. (2019). Advances and open problems in federated learning. arXiv preprint arXiv:1912.04977.