mlsanityData to 7 Oct 2026

FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learning

arXiv 2211.16669 ↗First posted 30 Nov 2022cs.LG cs.CR cs.DCcited by 3

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Abstract (arXiv)

Federated learning (FL) has emerged as a solution to deal with the risk of privacy leaks in machine learning training. This approach allows a variety of mobile devices to collaboratively train a machine learning model without sharing the raw on-device training data with the cloud. However, efficient edge deployment of FL is challenging because of the system/data heterogeneity and runtime variance. This paper optimizes the energy-efficiency of FL use cases while guaranteeing model convergence, by accounting for the aforementioned challenges. We propose FedGPO based on a reinforcement learning, which learns how to identify optimal global parameters (B, E, K) for each FL aggregation round adapting to the system/data heterogeneity and stochastic runtime variance. In our experiments, FedGPO improves the model convergence time by 2.4 times, and achieves 3.6 times higher energy efficiency over the baseline settings, respectively.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 72 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

27 of 72 references.

References · 27

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15 more
  1. 2017Don't Decay the Learning Rate, Increase the Batch Sizecited by 313
  2. 2017Train longer, generalize better: closing the generalization gap in large batch training of neural networkscited by 278
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  15. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4

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Cited by year

Citing papers per year: 2023: 2; 2024: 1.

3 papers.

  1. 2024Training Machine Learning models at the Edge: A Surveycited by 11
  2. 2023Green Federated Learningcited by 8
  3. 2023Exploring and Exploiting Data Heterogeneity in Recommendationcited by 1