Federated Learning (FL) is a type of distributed machine learning, which avoids sharing privacy and sensitive data with a central server. Despite the advances in FL, current approaches cannot provide satisfactory performance when dealing with heterogeneity in data and unpredictability of system devices. First, straggler devices can adversely impact convergence speed of the global model training. Second, for model aggregation in traditional FL, edge devices communicate frequently with a central server using their local updates.

IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS),

IEEE

978-1-6654-7137-4

https://ieeexplore.ieee.org/document/9935015