A-LAQ: Adaptive Lazily Aggregated Quantized Gradient
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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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 9 of its 20 references that are in the map. First posted on arXiv 31 Oct 2022.
Builds on
9 of 20 references.
References · 9
- 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
- 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
- 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
- 2020UVeQFed: Universal Vector Quantization for Federated Learningcited by 103
- 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
- 2021Adaptive Quantization of Model Updates for Communication-Efficient Federated Learningcited by 60
- 2020Federated Learning With Quantized Global Model Updatescited by 54
- 2020Adaptive Federated Learning With Gradient Compression in Uplink NOMAcited by 19
- 2022FedCau: A Proactive Stop Policy for Communication and Computation Efficient Federated Learningcited by 4
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Citing papers per year: 2023: 1; 2024: 2.
3 papers.
- 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regretcited by 7
- 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networkscited by 1
- 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantizationcited by 1
- 2024Accelerating Energy-Efficient Federated Learning in Cell-Free Networks with Adaptive Quantization30 Dec 2024
- 2024Adaptive Quantization Resolution and Power Control for Federated Learning over Cell-free Networks14 Dec 2024
- 2023A Communication-Efficient Adaptive Algorithm for Federated Learning under Cumulative Regret21 Jan 2023