| Abstract |
Federated learning (FL) has become a common approach for distributed training of quality of service (QoS) prediction tasks in smart city solutions. However, FL is vulnerable to heterogeneity. Existing FL-based QoS prediction methods usually update the global model by directly aggregating the divergent local gradients computed on heterogeneous datasets, which does not effectively capture the heterogeneity of users' QoS data distribution and requires many rounds of training and much communication time to achieve satisfactory accuracy. In addition, the high communication latency between the central server and the client further slows down the convergence process. To address the above issues, we propose a multi-centre federated learning framework for services QoS prediction via a cloud-edge collaboration mechanism, namely MultiFed. In this framework, we sink the central server in traditional FL from cloud to edge and train a global model in each edge region. The edge server performs two gradient aggregation strategies to update the global model in each training round. One is an internal aggregation strategy for regional users; the other is an external aggregation among edge servers with the assistance of a cloud server. Extensive experiments have shown that 9.52%-40.00% reduces the communication delay, and 1.91%-38.89% reduces the communication rounds required to reach the target prediction in this method compared to existing methods, validating the effectiveness of MultiFed. (c) 2023 Elsevier B.V. All rights reserved. |
