Aditya Pribadi Kalapaaking

RAD-SFL: Resource-Aware Dynamic Split Federated Learning for Heterogeneous Edge Environments with Non-IID Data

Edge computing enables distributed intelligence in resource-constrained IoT environments. However, traditional Federated Learning (FL) struggles with heterogeneous device capabilities, dynamic network conditions, and non-IID data distributions, resulting in straggler effects, slow convergence, and inefficient resource...