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Tackling Data Heterogeneity in Federated Learning
A persistent challenge in Federated Learning (FL) lies in handling statistical heterogeneity—namely, if the clients’ distributions are different from each other. Shinichi Nakajima, BIFOLD research Grouplead and his team propose FLOCO (Federated Learning over Connected Modes), to tackle those issues.
BIFOLD Ph.D. Student Receives Software Campus Funding
Muaid Mughrabi is a Ph.D. student specializing in Data Integration and Data Preparation at BIFOLD, Technische Universität Berlin, under the supervision of Prof. Dr. Ziawasch Abedjan. In the coming year, he will embark on a research project in collaboration with Celonis.
Machine learning accelerates catalyst discovery
Machine learning (ML) models have recently become popular in the field of heterogeneous catalyst design.
Machine Learning Backdoors in Hardware
So-called backdoor attacks pose a serioues threat to machine learning, as they can compromise the integrity of security-critical AI systems, such as those used in autonomous driving or healthcare.
Paper Forecast NeurIPS 2024
Several BIFOLD research groups participate in the 48th Annual Conference on Neural Information Processing Systems in Vancouver, Canada, taking place from December 10 to 15, 2024.
BIFOLD Workshop examines European AI Act challenges
On November 29th, 2024, Bifold hosted an engaging workshop with interdisciplinary experts to explore the challenges and opportunities presented by the new European AI Act.
Klaus-Robert Müller on "Highly Cited Researchers" list
Since 2019, Prof. Dr. Klaus-Robert Müller has consistently appeared on the "Highly Cited" list, affirming his lasting contribution to groundbreaking research.
Researcher Spotlight: Dr. Lisa Raithel
Dr. Lisa Raithel is working at the integration of natural language processing (NLP) and medicine. Recently completing her PhD, she dives deep into the multilingual analysis of health data to uncover the real-world effects of medications.
Explainable AI illuminates the course of history
Understanding the evolution and dissemination of human knowledge over time is a long-cherished dream of many historians. A dream that faced many challenges due to the abundance of historical materials and limited specialist resources. However, the digitization of many historical archives presents new opportunities for AI-supported analysis.
AI in medicine: new approach for more efficient diagnostics
Researchers from LMU, BIFOLD, and Charité have developed a new AI tool that uses imaging data to also detect less frequent diseases of the gastrointestinal tract. In contrast to conventional models, the new AI only needs training data from common findings to detect deviations.