However, FL faces several key challenges: communication overhead (caused by unreliable, bandwidth-limited client-side links such as Wi-Fi/BLE/LPWAN), severe heterogeneity in client hardware and…
Research Field
Federated Learning (FL) is a game-changing paradigm in distributed learning. It uses edge devices as clients to train a global model by leveraging the diversity of local datasets…
Research Field
Federated Learning (FL) is a game-changing paradigm in distributed learning. It uses edge devices as clients to train a global model by leveraging the diversity of local datasets…
Employee responsible
Prof. Dr. Akash Kumar (head of supervision) Dr-Ing. Zahra Ebrahimi (project manager and research associate) M.Sc. Maryam Eslami (research associate)
Project Description
Energy efficiency and real-time performance are among the main challenges in the 5G/6G era, driven by the increasing complexity of computational algorithms in stream processing…
Key contributions
Cross-layer approximation methodology and architecture guided by various heuristics and ML models. Design of heterogeneous approximate hardware…
