RIS-Assisted Wireless FL Optimizes Convergence and Latency
Summary
This paper proposes an adaptive modulation and resource allocation scheme for RIS-assisted wireless Federated Learning (FL) to address training latency and convergence issues under unreliable transmission. It derives a convergence-related upper bound considering symbol errors and formulates a joint optimization problem, showing improved convergence and accuracy.
Why it matters
Professionals deploying Federated Learning in real-world wireless environments can use this research to design more efficient and reliable systems that balance communication latency with model convergence and accuracy.
How to implement this in your domain
- 1Evaluate the feasibility of integrating Reconfigurable Intelligent Surfaces (RISs) into your wireless FL infrastructure.
- 2Implement adaptive modulation and sub-channel allocation strategies based on the derived convergence-latency trade-offs.
- 3Develop mechanisms to characterize and account for symbol error rates (SER) in your FL loss decay models.
- 4Explore the proposed hybrid alternating optimization framework for joint convergence-latency optimization in your FL deployments.
Who benefits
Key takeaways
- RIS-assisted wireless FL can significantly improve training latency and convergence.
- The research characterizes the impact of symbol errors on FL loss decay.
- A new optimization scheme balances communication delay and learning convergence.
- The proposed method achieves faster convergence and higher accuracy in challenging wireless scenarios.
Original post by Liwei Wang, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xusheng Zhu, Qiong Wu
"arXiv:2607.19759v1 Announce Type: new Abstract: Federated learning (FL) over wireless networks suffers from significant training latency and degraded convergence due to unreliable wireless transmission, especially under blocked propagation environments. Although reconfigurable in…"
View on XOriginally posted by Liwei Wang, Wen Chen, Jun Li, Qingqing Wu, Ming Ding, Xusheng Zhu, Qiong Wu on X · view source
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