Work

Over-the-Air Federated Learning in Cell-Free MIMO With Long-Term Power Constraint

Year: 2025

Type: article

Abstract: Wireless networks supporting artificial intelligence have gained significant attention, with Over-the-Air Federated Learning (OTA-FL) emerging as a key application due to its unique transmission and distributed computing characteristics. This letter derives error bounds for OTA-FL in a cell-free MIMO system and formulates an optimization problem to minimize the optimality gap via the joint optimization of transmit and receive beamforming. We introduce the MOP-LOFPC algorithm, which employs Lyapunov optimization to decouple long-term constraints across rounds while requiring only causal channel state information. Experimental results demonstrate that MOP-LOFPC achieves a better and more flexible trade-off between the model’s training loss and adherence to long-term power constraints compared to existing baselines.

Source: IEEE Wireless Communications Letters

DOI: 10.1109/lwc.2025.3539683

Authors: Yifan Wang, Cheng Zhang, Yuandong Zhuang, Mingzeng Dai, Haiming Wang, Yongming Huang

Institutions: Southeast University, Purple Mountain Laboratories, Lenovo (China)

Language: English

Topic: Advanced Wireless Communication Technologies

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province