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Cost-Effective Federated Learning Design

arXiv 2012.08336 ↗First posted 15 Dec 2020cs.LG cs.DC cs.NIcited by 62

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Abstract (arXiv)

Federated learning (FL) is a distributed learning paradigm that enables a large number of devices to collaboratively learn a model without sharing their raw data. Despite its practical efficiency and effectiveness, the iterative on-device learning process incurs a considerable cost in terms of learning time and energy consumption, which depends crucially on the number of selected clients and the number of local iterations in each training round. In this paper, we analyze how to design adaptive FL that optimally chooses these essential control variables to minimize the total cost while ensuring convergence. Theoretically, we analytically establish the relationship between the total cost and the control variables with the convergence upper bound. To efficiently solve the cost minimization problem, we develop a low-cost sampling-based algorithm to learn the convergence related unknown parameters. We derive important solution properties that effectively identify the design principles for different metric preferences. Practically, we evaluate our theoretical results both in a simulated environment and on a hardware prototype. Experimental evidence verifies our derived properties and demonstrates that our proposed solution achieves near-optimal performance for various datasets, different machine learning models, and heterogeneous system settings.

Key facts. Cited by 62 papers in the map (data through 14 Oct 2026). First posted on arXiv 15 Dec 2020.

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Citing papers per year: 2021: 10; 2022: 16; 2023: 13; 2024: 13; 2025: 8; 2026: 2.

62 papers.

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  1. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  2. 2021Tackling System and Statistical Heterogeneity for Federated Learning with Adaptive Client Samplingcited by 66
  3. 2022A Survey on Heterogeneous Federated Learningcited by 42
  4. 2021Cost-Effective Federated Learning in Mobile Edge Networkscited by 25
  5. 2023Federated Domain Generalization: A Surveycited by 25
  6. 2023Every Parameter Matters: Ensuring the Convergence of Federated Learning with Dynamic Heterogeneous Models Reductioncited by 24
  7. 2024FIARSE: Model-Heterogeneous Federated Learning via Importance-Aware Submodel Extractioncited by 21
  8. 2025Ten Challenging Problems in Federated Foundation Modelscited by 20
  9. 2023AI-native Interconnect Framework for Integration of Large Language Model Technologies in 6G Systemscited by 19
  10. 2022Green, Quantized Federated Learning over Wireless Networks: An Energy-Efficient Designcited by 15
  11. 2021Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspectivecited by 14
  12. 2023DYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Datasetcited by 14
38 more
  1. 2021Efficient Federated Meta-Learning over Multi-Access Wireless Networkscited by 13
  2. 2022Federated Learning with Flexible Controlcited by 12
  3. 2021Semi-Synchronous Federated Learning for Energy-Efficient Training and Accelerated Convergence in Cross-Silo Settingscited by 11
  4. 2021Federated Few-Shot Learning with Adversarial Learningcited by 11
  5. 2021Resource-constrained Federated Edge Learning with Heterogeneous Data: Formulation and Analysiscited by 10
  6. 2021On the Tradeoff between Energy, Precision, and Accuracy in Federated Quantized Neural Networkscited by 10
  7. 2023Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classificationcited by 10
  8. 2022Service Delay Minimization for Federated Learning over Mobile Devicescited by 9
  9. 2024Adaptive Heterogeneous Client Sampling for Federated Learning over Wireless Networkscited by 9
  10. 2023Time-sensitive Learning for Heterogeneous Federated Edge Intelligencecited by 8
  11. 2023Incentive Mechanism Design for Unbiased Federated Learning with Randomized Client Participationcited by 8
  12. 2022Federated Momentum Contrastive Clusteringcited by 7
  13. 2022Addressing Heterogeneity in Federated Learning via Distributional Transformationcited by 6
  14. 2023Multi-Source to Multi-Target Decentralized Federated Domain Adaptationcited by 6
  15. 2022Towards Communication-Learning Trade-off for Federated Learning at the Network Edgecited by 5
  16. 2022A Fast Blockchain-based Federated Learning Framework with Compressed Communicationscited by 5
  17. 2022Joint Optimization of Energy Consumption and Completion Time in Federated Learningcited by 5
  18. 2022ModularFed: Leveraging Modularity in Federated Learning Frameworkscited by 4
  19. 2024PrivSGP-VR: Differentially Private Variance-Reduced Stochastic Gradient Push with Tight Utility Boundscited by 4
  20. 2022TinyMLOps: Operational Challenges for Widespread Edge AI Adoptioncited by 3
  21. 2022Adaptive Control of Client Selection and Gradient Compression for Efficient Federated Learningcited by 3
  22. 2023Distributed Traffic Synthesis and Classification in Edge Networks: A Federated Self-supervised Learning Approachcited by 3
  23. 2024Decentralized Personalized Federated Learning based on a Conditional Sparse-to-Sparser Schemecited by 3
  24. 2022CC-FedAvg: Computationally Customized Federated Averagingcited by 2
  25. 2023Benchmarking Collaborative Learning Methods Cost-Effectiveness for Prostate Segmentationcited by 2
  26. 2023Straggler-resilient Federated Learning: Tackling Computation Heterogeneity with Layer-wise Partial Model Training in Mobile Edge Networkcited by 2
  27. 2024Heterogeneity-Aware Cooperative Federated Edge Learning with Adaptive Computation and Communication Compressioncited by 2
  28. 2021Optimizing the Numbers of Queries and Replies in Federated Learning with Differential Privacycited by 1
  29. 2022Suppressing Noise from Built Environment Datasets to Reduce Communication Rounds for Convergence of Federated Learningcited by 1
  30. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  31. 2023Achieving Sample and Computational Efficient Reinforcement Learning by Action Space Reduction via Groupingcited by 1
  32. 2023Honest Score Client Selection Scheme: Preventing Federated Learning Label Flipping Attacks in Non-IID Scenarioscited by 1
  33. 2024Fed-CVLC: Compressing Federated Learning Communications with Variable-Length Codescited by 1
  34. 2024Adaptive Federated Learning in Heterogeneous Wireless Networks with Independent Samplingcited by 1
  35. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1
  36. 2024Geographical Node Clustering and Grouping to Guarantee Data IIDness in Federated Learningcited by 1
  37. 2024Adaptive Client Selection with Personalization for Communication Efficient Federated Learningcited by 1
  38. 2025Embedded Federated Feature Selection with Dynamic Sparse Training: Balancing Accuracy-Cost Tradeoffscited by 1

50 most cited of 62 All 62 (JSON)