Summary of Accelerating Energy-efficient Federated Learning in Cell-free Networks with Adaptive Quantization, by Afsaneh Mahmoudi et al.
Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization
by Afsaneh Mahmoudi, Ming Xiao, Emil Björnson
First submitted to arxiv on: 30 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an energy-efficient and low-latency Federated Learning (FL) framework that enables clients to share learning parameters instead of local data, reducing communication overhead. The framework utilizes Cell-Free Massive MIMO (CFmMIMO) to serve multiple clients on shared resources, boosting spectral efficiency and reducing latency for large-scale FL. To address the challenge of clients’ communication resource limitations hindering the completion of FL training, the paper proposes an optimized uplink power allocation scheme that dynamically adjusts bit allocation for local gradient updates to reduce communication costs. The framework also employs an adaptive quantization scheme to balance energy and latency, solving a joint optimization problem using sequential quadratic programming (SQP). Additionally, clients use the AdaDelta method for local FL model updates, enhancing local model convergence compared to standard SGD. The paper provides a comprehensive analysis of FL convergence with AdaDelta local updates and shows that the proposed power allocation scheme outperforms existing methods in terms of test accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to make machines learn together without sharing all their data. This helps reduce the amount of information they need to send each other, making it faster and more efficient. The method uses special wireless technology called Cell-Free Massive MIMO (CFmMIMO) that allows many devices to share resources and communicate quickly. To make sure this works well, the paper proposes a new way to decide how much power devices should use when sending information, which helps reduce the amount of data they need to send. The paper also uses a special method called AdaDelta to help devices learn better from their own data. This leads to more accurate results and faster learning. |
Keywords
» Artificial intelligence » Boosting » Federated learning » Optimization » Quantization
