mlsanityData to 7 Oct 2026

BIPPO: Budget-Aware Independent PPO for Energy-Efficient Federated Learning Services

arXiv 2511.08142 ↗First posted 11 Nov 2025cs.LG cs.DC cs.MAcited by 0

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Abstract (arXiv)

Federated Learning (FL) is a promising machine learning solution in large-scale IoT systems, guaranteeing load distribution and privacy. However, FL does not natively consider infrastructure efficiency, a critical concern for systems operating in resource-constrained environments. Several Reinforcement Learning (RL) based solutions offer improved client selection for FL; however, they do not consider infrastructure challenges, such as resource limitations and device churn. Furthermore, the training of RL methods is often not designed for practical application, as these approaches frequently do not consider generalizability and are not optimized for energy efficiency. To fill this gap, we propose BIPPO (Budget-aware Independent Proximal Policy Optimization), which is an energy-efficient multi-agent RL solution that improves performance. We evaluate BIPPO on two image classification tasks run in a highly budget-constrained setting, with FL clients training on non-IID data, a challenging context for vanilla FL. The improved sampler of BIPPO enables it to increase the mean accuracy compared to non-RL mechanisms, traditional PPO, and IPPO. In addition, BIPPO only consumes a negligible proportion of the budget, which stays consistent even if the number of clients increases. Overall, BIPPO delivers a performant, stable, scalable, and sustainable solution for client selection in IoT-FL.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 53 references that are in the map. First posted on arXiv 11 Nov 2025.

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References · 17

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2017Proximal Policy Optimization Algorithmscited by 14,441
  3. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  4. 2015Deep Reinforcement Learning with Double Q-learningcited by 1,704
  5. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  6. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  7. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 281
  8. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  9. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
  10. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  11. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  12. 2022A Multi-agent Reinforcement Learning Approach for Efficient Client Selection in Federated Learningcited by 28
5 more
  1. 2023Part-time Power Measurements: nvidia-smi's Lack of Attentioncited by 15
  2. 2023Accuracy is not the only Metric that matters: Estimating the Energy Consumption of Deep Learning Modelscited by 9
  3. 2024Federated Learning as a Service for Hierarchical Edge Networks with Heterogeneous Modelscited by 6
  4. 2024A Beginner's Guide to Power and Energy Measurement and Estimation for Computing and Machine Learningcited by 3
  5. 2024Reactive Orchestration for Hierarchical Federated Learning Under a Communication Cost Budgetcited by 1

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