mlsanityData to 7 Oct 2026

AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learning

arXiv 2107.08147 ↗First posted 16 Jul 2021cs.LG cs.DCcited by 39

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Abstract (arXiv)

Federated learning enables a cluster of decentralized mobile devices at the edge to collaboratively train a shared machine learning model, while keeping all the raw training samples on device. This decentralized training approach is demonstrated as a practical solution to mitigate the risk of privacy leakage. However, enabling efficient FL deployment at the edge is challenging because of non-IID training data distribution, wide system heterogeneity and stochastic-varying runtime effects in the field. This paper jointly optimizes time-to-convergence and energy efficiency of state-of-the-art FL use cases by taking into account the stochastic nature of edge execution. We propose AutoFL by tailor-designing a reinforcement learning algorithm that learns and determines which K participant devices and per-device execution targets for each FL model aggregation round in the presence of stochastic runtime variance, system and data heterogeneity. By considering the unique characteristics of FL edge deployment judiciously, AutoFL achieves 3.6 times faster model convergence time and 4.7 and 5.2 times higher energy efficiency for local clients and globally over the cluster of K participants, respectively.

Key facts. Cited by 39 papers in the map (data through 7 Oct 2026). Builds on 53 of its 142 references that are in the map. First posted on arXiv 16 Jul 2021.

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53 of 142 references.

References · 53

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38 more
  1. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,097
  2. 2014Striving for Simplicity: The All Convolutional Netcited by 983
  3. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  4. 2019Towards Federated Learning at Scale: System Designcited by 826
  5. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  6. 2017Federated Multi-Task Learningcited by 669
  7. 2017In-Datacenter Performance Analysis of a Tensor Processing Unitcited by 610
  8. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
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  14. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  15. 2019Fair Resource Allocation in Federated Learningcited by 389
  16. 2019Improving Sample Efficiency in Model-Free Reinforcement Learning from Imagescited by 268
  17. 2021ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learningcited by 253
  18. 2021No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Datacited by 241
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  22. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
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  25. 2020LotteryFL: Personalized and Communication-Efficient Federated Learning with Lottery Ticket Hypothesis on Non-IID Datasetscited by 60
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 11; 2023: 6; 2024: 12; 2025: 6; 2026: 2.

39 papers.

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  1. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  2. 2022Measuring the Carbon Intensity of AI in Cloud Instancescited by 110
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2023Federated Learning for Computationally-Constrained Heterogeneous Devices: A Surveycited by 40
  5. 2023Green Edge AI: A Contemporary Surveycited by 24
  6. 2023Machine Learning for Large-Scale Optimization in 6G Wireless Networkscited by 18
  7. 2022Towards Fair Federated Recommendation Learning: Characterizing the Inter-Dependence of System and Data Heterogeneitycited by 17
  8. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  9. 2024Green Federated Learning: A new era of Green Aware AIcited by 12
  10. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
  11. 2023FLrce: Resource-Efficient Federated Learning with Early-Stopping Strategycited by 10
  12. 2021Towards Efficient Synchronous Federated Training: A Survey on System Optimization Strategiescited by 9
27 more
  1. 2024FedGCS: A Generative Framework for Efficient Client Selection in Federated Learning via Gradient-based Optimizationcited by 9
  2. 2022Pisces: Efficient Federated Learning via Guided Asynchronous Trainingcited by 8
  3. 2023Green Federated Learningcited by 8
  4. 2024Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutionscited by 8
  5. 2022Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systemscited by 6
  6. 2025Agentic Software Issue Resolution with Large Language Models: A Surveycited by 6
  7. 2024Adaptive Parameter-Efficient Federated Fine-Tuning on Heterogeneous Devicescited by 5
  8. 2022FEL: High Capacity Learning for Recommendation and Ranking via Federated Ensemble Learningcited by 4
  9. 2022FedGPO: Heterogeneity-Aware Global Parameter Optimization for Efficient Federated Learningcited by 3
  10. 2024Energy-Efficient Channel Decoding for Wireless Federated Learning: Convergence Analysis and Adaptive Designcited by 3
  11. 2022Hybrid Learning for Orchestrating Deep Learning Inference in Multi-user Edge-cloud Networkscited by 2
  12. 2022CC-FedAvg: Computationally Customized Federated Averagingcited by 2
  13. 2024FedCore: Straggler-Free Federated Learning with Distributed Coresetscited by 2
  14. 2024Ranking-based Client Selection with Imitation Learning for Efficient Federated Learningcited by 2
  15. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  16. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  17. 2024SusFL: Energy-Aware Federated Learning-based Monitoring for Sustainable Smart Farmscited by 1
  18. 2024HeteroSwitch: Characterizing and Taming System-Induced Data Heterogeneity in Federated Learningcited by 1
  19. 2024Energy-Aware Decentralized Learning with Intermittent Model Trainingcited by 1
  20. 2024Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edgecited by 1
  21. 2025Onboard Optimization and Learning: A Surveycited by 1
  22. 2023REWAFL: Residual Energy and Wireless Aware Participant Selection for Efficient Federated Learning over Mobile Devicescited by 0
  23. 2025Tackling Heterogeneity in Federated Learning via Variance-Reduced Boltzmann Sampling within Homogeneous Social Coalitionscited by 0
  24. 2025DART: A Server-side Plug-in for Resource-efficient Robust Federated Learningcited by 0
  25. 2025UnifiedFL: A Dynamic Unified Learning Framework for Equitable Federationcited by 0
  26. 2026Auto-FL-Research: Agentic Search for Federated Learning Algorithmscited by 0
  27. 2026Capacity Confounds and Coverage Guarantees in Adaptive Sub-model Federated Learningcited by 0