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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.
Photo recap: BIFOLD New Year's reception
At its New Year's reception BIFOLD welcomed a series of distinguished guests and friends from Berlin's AI community.
Photo recap: All Hands Meeting 2023
On October 9 and 10, 2023, BIFOLD welcomed the other Geman AI centers (ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, MCML, and the DFKI) in Berlin. The annual meeting featured guests, partners, visitors, and researchers from all over Germany.
AI centers are the foundation of the German AI ecosystem
On October 9th and 10th, 2023, the Berlin Institute for the Foundations of Learning and Data (BIFOLD) at TU Berlin invited scientists from the university AI competence centers (BIFOLD, ScaDS.AI Dresden/Leipzig, Lamarr Institute, Tübingen AI Center, and MCML) and the DFKI to Berlin to present and discuss the latest results of their research on the EUREF campus.