학술논문
A Traffic Model Based Approach to Parameter Server Design in Federated Learning Processes
Document Type
Periodical
Author
Source
IEEE Communications Letters IEEE Commun. Lett. Communications Letters, IEEE. 27(7):1774-1778 Jul, 2023
Subject
Language
ISSN
1089-7798
1558-2558
2373-7891
1558-2558
2373-7891
Abstract
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.