# A-LAQ: Adaptive Lazily Aggregated Quantized Gradient

arXiv: https://arxiv.org/abs/2210.17474 · HTML: https://mlsanity.com/paper/2210.17474/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 31 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Robust and Communication-Efficient Federated Learning from Non-IID Data](https://mlsanity.com/paper/1903.02891/) (2019)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approach](https://mlsanity.com/paper/2001.04756/) (2020)
- [Adaptive Quantization of Model Updates for Communication-Efficient Federated Learning](https://mlsanity.com/paper/2102.04487/) (2021)
- [Federated Learning With Quantized Global Model Updates](https://mlsanity.com/paper/2006.10672/) (2020)
- [Adaptive Federated Learning With Gradient Compression in Uplink NOMA](https://mlsanity.com/paper/2003.01344/) (2020)
- [FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learning](https://mlsanity.com/paper/2204.07773/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret](https://mlsanity.com/paper/2301.08869/) (2023)
- [Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks](https://mlsanity.com/paper/2412.10878/) (2024)
- [Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization](https://mlsanity.com/paper/2412.20785/) (2024)

## Abstract (arXiv)

Federated Learning (FL) plays a prominent role in solving machine learning problems with data distributed across clients. In FL, to reduce the communication overhead of data between clients and the server, each client communicates the local FL parameters instead of the local data. However, when a wireless network connects clients and the server, the communication resource limitations of the clients may prevent completing the training of the FL iterations. Therefore, communication-efficient variants of FL have been widely investigated. Lazily Aggregated Quantized Gradient (LAQ) is one of the promising communication-efficient approaches to lower resource usage in FL. However, LAQ assigns a fixed number of bits for all iterations, which may be communication-inefficient when the number of iterations is medium to high or convergence is approaching. This paper proposes Adaptive Lazily Aggregated Quantized Gradient (A-LAQ), which is a method that significantly extends LAQ by assigning an adaptive number of communication bits during the FL iterations. We train FL in an energy-constraint condition and investigate the convergence analysis for A-LAQ. The experimental results highlight that A-LAQ outperforms LAQ by up to a 50% reduction in spent communication energy and an 11% increase in test accuracy.