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 utilization. This paper proposes Resource-Aware Dynamic Split Federated Learning (RAD-SFL), a framework for efficient distributed training in heterogeneous edge environments. RAD-SFL introduces a dynamic model layer splitting mechanism that adaptively partitions model execution between client devices and edge servers based on real-time computation and communication conditions, and a group-and-reorder technique that organizes devices into balanced groups with similar data distributions to improve model convergence under non-IID settings. We validate RAD-SFL through experiments on widely adopted datasets using both a simulated environment and a real testbed with heterogeneous IoT devices. Results demonstrate that RAD-SFL reduces the training time by up to 66.4%, decreases device-side energy consumption by 52.5%, and improves global model accuracy by up to 30.5% compared to FL and SFL baselines.
Aditya Pribadi Kalapaaking, Veronika Stephanie, Eric Samikwa et al.· International Conference on...· 0 citations
G mobile networks are increasingly using Artificial Intelligence to manage highly dynamic environments characterized by time-varying traffic demands, user mobility, and heterogeneous resources. The dynamic behavior of User Equipment makes timely and accurate control decisions challenging, while distributed data exchange introduces communication overhead and privacy concerns. These challenges call for scalable and communication-efficient learning mechanisms for Radio Access Network (RAN) orchestration. In this paper, we propose DERRIC-FRL, a decentralized Federated Reinforcement Learning framework to orchestrate RAN intelligent controllers. DERRIC-FRL jointly optimizes controller placement and user power allocation through a selective two-level aggregation mechanism, reducing data exchange to only selected orchestrators and controllers while preserving user privacy and improving overall network performance. Specifically, our method significantly reduces total training communication costs by 34% across inter-domain connections, and up to 77% across intra-domain connections, compared to the FedAvg approach. Furthermore, DERRIC-FRL improves user throughput by up to 53% and 61% compared to the DERRIC and FedAvg baselines across a broad range of simulated scenarios.
Elham Hashemi Nezhad, Eric Samikwa, Torsten Braun· IEEE Conference on Network S...· 0 citations