A Traffic Model Based Approach to Parameter Server Design in Federated Learning Processes.

This letter proposes a model to describe the data traffic generated by a Federated Learning (FL) process in a wireless network with asynchronous Parameter Server (PS) orchestration and heterogeneous clients. The model accounts for the local update processes implemented by individual clients and it is used to enforce requirements on the PS design, namely to regulate the interval among consecutive global model updates. PS requirements are validated on realistic pools of resource-constrained wireless edge devices, typically found in Internet-of-Things (IoT) setups. Numerical results show that the proposed policy is effective when devices have unbalanced resources, namely, different sample distributions and computational capabilities. It permits an accuracy gain of up to 15-17% on average with respect to typical asynchronous PS designs.