Complete workshop: Federated AI with Flower

Описание: Federated AI offers a promising path toward building secure, collaborative, and adaptive systems without the need to centralize sensitive data. In this tutorial, we introduce the Flower framework and showcase its use in scenarios such as anomaly detection, intrusion defense, and scalable federated deployments. Through hands-on examples, participants will gain practical insights into designing resilient Federated AI solutions while also reflecting on open challenges and future research directions for trustworthy and secure machine learning.

Facilitator Bios:
William Lindskog-Münzing: William is a Solutions Engineer at Flower Labs, with industry experience in the heavy-asset industries. His research experience at TU Munich includes federated learning for tabular data, and machine learning application in the automotive industry. He recently led the research and development team at TUM.ai, Germany's leading student AI lab. William also contributed with FedPer baseline during Flower's 2023 Summer of Reproducibility initiative.

Dimitris Stripelis
Dimitris Stripelis is a Research Engineer at Flower Labs, with industry experience at Amazon AWS, and Salesforce. He earned his PhD from the University of Southern California and worked as a Postdoctoral Researcher at USC's Information Sciences Institute (USC-ISI). He is a recipient of the USC Myronis Fellowship (2020) and the A.G. Leventis Foundation Educational Grant (2019–2021). His research focuses on federated and distributed machine learning. He has served as a reviewer for many conferences, including NeurIPS, ICML, AISTATS, AAAI, EMNLP, ECAI, and WISE, as well as journals such as TNNLS, TMI, and TKDE. He co-organized the first Federated Learning Systems (FLSys) workshop at MLSys 2023 and the Federated Learning on the Edge (FLEDGE) Symposium at AAAI 2024.