Eric Samikwa

Decentralized Vertical Federated Learning via Graph-Based Learning Consensus

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...

Decentralized Federated Multi-Agent Reinforcement Learning for RAN Controller Orchestration in 6G

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 exchang...