# Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks

arXiv: https://arxiv.org/abs/2412.10878 · HTML: https://mlsanity.com/paper/2412.10878/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 27 references that are in the map.
- First posted on arXiv 14 Dec 2024.

## Builds on (most cited first, 18 of 18)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Gradient Sparsification for Communication-Efficient Distributed Optimization](https://mlsanity.com/paper/1710.09854/) (2017)
- [Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.04487/) (2021)
- [Wireless Communications for Collaborative Federated Learning](https://mlsanity.com/paper/2006.02499/) (2020)
- [DAdaQuant: Doubly-adaptive quantization for communication-efficient Federated Learning](https://mlsanity.com/paper/2111.00465/) (2021)
- [FedDQ: Communication-Efficient Federated Learning with Descending Quantization](https://mlsanity.com/paper/2110.02291/) (2021)
- [Foundations of User-Centric Cell-Free Massive MIMO](https://mlsanity.com/paper/2108.02541/) (2021)
- [SAFARI: Sparsity enabled Federated Learning with Limited and Unreliable Communications](https://mlsanity.com/paper/2204.02321/) (2022)
- [AQUILA: Communication Efficient Federated Learning with Adaptive Quantization in Device Selection Strategy](https://mlsanity.com/paper/2308.00258/) (2023)
- [Energy-Efficient Power Control: A Look at 5G Wireless Technologies](https://mlsanity.com/paper/1503.04609/) (2015)
- [FedAQ: Communication-Efficient Federated Edge Learning via Joint Uplink and Downlink Adaptive Quantization](https://mlsanity.com/paper/2406.18156/) (2024)
- [FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning](https://mlsanity.com/paper/2204.07773/) (2022)
- [How Does Cell-Free Massive MIMO Support Multiple Federated Learning Groups?](https://mlsanity.com/paper/2107.09577/) (2021)
- [A-LAQ: Adaptive Lazily Aggregated Quantized Gradient](https://mlsanity.com/paper/2210.17474/) (2022)
- [Joint Compression and Deadline Optimization for Wireless Federated Learning](https://mlsanity.com/paper/2305.03969/) (2023)
- [Joint Energy and Latency Optimization in Federated Learning over Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2404.18287/) (2024)

## Built on by (most cited first, 1 of 1)

- [Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization](https://mlsanity.com/paper/2412.20785/) (2024)

## Abstract (arXiv)

Federated learning (FL) is a distributed learning framework where users train a global model by exchanging local model updates with a server instead of raw datasets, preserving data privacy and reducing communication overhead. However, the latency grows with the number of users and the model size, impeding the successful FL over traditional wireless networks with orthogonal access. Cell-free massive multiple-input multipleoutput (CFmMIMO) is a promising solution to serve numerous users on the same time/frequency resource with similar rates. This architecture greatly reduces uplink latency through spatial multiplexing but does not take application characteristics into account. In this paper, we co-optimize the physical layer with the FL application to mitigate the straggler effect. We introduce a novel adaptive mixed-resolution quantization scheme of the local gradient vector updates, where only the most essential entries are given high resolution. Thereafter, we propose a dynamic uplink power control scheme to manage the varying user rates and mitigate the straggler effect. The numerical results demonstrate that the proposed method achieves test accuracy comparable to classic FL while reducing communication overhead by at least 93% on the CIFAR-10, CIFAR-100, and Fashion-MNIST datasets. We compare our methods against AQUILA, Top-q, and LAQ, using the max-sum rate and Dinkelbach power control schemes. Our approach reduces the communication overhead by 75% and achieves 10% higher test accuracy than these benchmarks within a constrained total latency budget.