Summary of Adaptive Quantization Resolution and Power Control For Federated Learning Over Cell-free Networks, by Afsaneh Mahmoudi et al.
Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks
by Afsaneh Mahmoudi, Emil Björnson
First submitted to arxiv on: 14 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Networking and Internet Architecture (cs.NI); Signal Processing (eess.SP)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Federated learning (FL) is a decentralized framework that enables users to train global models without sharing raw data, preserving privacy while reducing communication overhead. However, FL’s latency grows with the number of users and model size, hindering its adoption over traditional wireless networks. To address this challenge, researchers propose a novel co-optimization approach that combines cell-free massive multiple-input multiple-output (CFmMIMO) architecture with application characteristics. A key innovation is an adaptive mixed-resolution quantization scheme for local gradient updates, which prioritizes high-resolution encoding of essential entries. Additionally, a dynamic uplink power control scheme manages varying user rates and mitigates the straggler effect. The proposed method achieves test accuracy comparable to classic FL while reducing communication overhead by at least 93% on popular datasets (CIFAR-10, CIFAR-100, Fashion-MNIST). The approach outperforms benchmarks like AQUILA, Top-q, and LAQ in terms of max-sum rate and Dinkelbach power control schemes. By co-optimizing the physical layer with the FL application, this research reduces communication overhead by 75% and boosts test accuracy by 10%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Federated learning is a way to train models on many devices without sharing their data, which helps keep information private. However, it can be slow if there are too many devices or large models. To speed things up, researchers combined two ideas: cell-free massive multiple-input multiple-output (CFmMIMO) and adaptive quantization. The key innovation is prioritizing the most important parts of the model updates to reduce data size. They also developed a way to manage power usage based on how fast each device can communicate. This approach achieves similar accuracy as traditional methods while reducing data transmission by at least 93%. It even outperforms other benchmark approaches! |
Keywords
» Artificial intelligence » Federated learning » Optimization » Quantization
