Joint topology and beamforming optimization for decentralized federated learning
Author Identifier (ORCID)
Abstract
Decentralized Federated Learning (DFL) enables collaborative model training without central coordination. However, DFL faces challenges in dynamic networks, where existing methods struggle to balance consensus rate and communication efficiency, while overlooking practical issues such as topology variation. This paper presents Dynamic AirComp-enabled DFL (DA-DFL), a novel framework that integrates over-the-air computation (AirComp) with the BASE-GRAPH consensus algorithm for efficient DFL over dynamic topologies. The convergence analysis for DA-DFL under dynamic settings is conducted to reveal the influence of the consensus period and communication errors. We define communication overhead metrics, and jointly optimize transceiver beamformers and dynamic topologies. A topology matching algorithm is developed to reduce communication overhead by aligning logical and physical topologies. Experiments show significant gains of DA-DFL in communication efficiency, e.g., reducing communication links and distances by up to 42% and 50%, respectively, compared to benchmarks.
Keywords
Consensus problem, decentralized federated learning, dynamic topology, over-the-air computation (AirComp)
Document Type
Journal Article
Date of Publication
1-1-2026
Volume
25
Publication Title
IEEE Transactions on Wireless Communications
Publisher
IEEE
School
School of Engineering
Funders
National Natural Science Foundation of China (B62271352, 42171404) / Fundamental Research Funds for the Central Universities (22120250094)
Copyright
subscription content
First Page
12945
Last Page
12961
Comments
Feng, H., Wang, R., Liu, E., Ni, W., Niyato, D., & Jamalipour, A. (2026). Joint topology and beamforming optimization for decentralized federated learning. IEEE Transactions on Wireless Communications, 25, 12945–12961. https://doi.org/10.1109/TWC.2026.3667951