Over-the-Air Federated Learning in Cell-Free MIMO with Long-term Power Constraint
Not on the map. Why?
Abstract (arXiv)
Wireless networks supporting artificial intelligence have gained significant attention, with Over-the-Air Federated Learning emerging as a key application due to its unique transmission and distributed computing characteristics. This paper derives error bounds for Over-the-Air Federated Learning in a Cell-free MIMO system and formulates an optimization problem to minimize optimality gap via joint optimization of power control and 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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 15 references that are in the map. First posted on arXiv 7 Oct 2024.
Builds on
7 of 15 references.
References · 7
- 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
- 2019A Survey on Federated Learning Systems: Vision, Hype and Reality for Data Privacy and Protectioncited by 374
- 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 63
- 2021Dynamic Scheduling for Over-the-Air Federated Edge Learning with Energy Constraintscited by 36
- 2021Learning Rate Optimization for Federated Learning Exploiting Over-the-air Computationcited by 29
- 2023Joint Antenna Selection and Beamforming for Massive MIMO-enabled Over-the-Air Federated Learningcited by 3
- 2022Over-The-Air Federated Learning Over Scalable Cell-free Massive MIMOcited by 2
Built on by
Not cited yet.