mlsanityData to 7 Oct 2026

CC-FedAvg: Computationally Customized Federated Averaging

arXiv 2212.13679 ↗First posted 28 Dec 2022cs.LG cs.AI cs.DCcited by 2

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) is an emerging paradigm to train model with distributed data from numerous Internet of Things (IoT) devices. It inherently assumes a uniform capacity among participants. However, due to different conditions such as differing energy budgets or executing parallel unrelated tasks, participants have diverse computational resources in practice. Participants with insufficient computation budgets must plan for the use of restricted computational resources appropriately, otherwise they would be unable to complete the entire training procedure, resulting in model performance decline. To address this issue, we propose a strategy for estimating local models without computationally intensive iterations. Based on it, we propose Computationally Customized Federated Averaging (CC-FedAvg), which allows participants to determine whether to perform traditional local training or model estimation in each round based on their current computational budgets. Both theoretical analysis and exhaustive experiments indicate that CC-FedAvg has the same convergence rate and comparable performance as FedAvg without resource constraints. Furthermore, CC-FedAvg can be viewed as a computation-efficient version of FedAvg that retains model performance while considerably lowering computation overhead.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 32 of its 45 references that are in the map. First posted on arXiv 28 Dec 2022.

Builds on

32 of 45 references.

References · 32

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  6. 2018Federated Learning with Non-IID Datacited by 1,099
  7. 2020Adaptive Federated Optimizationcited by 1,036
  8. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 897
  9. 2021Model-Contrastive Federated Learningcited by 762
  10. 2020Ensemble Distillation for Robust Model Fusion in Federated Learningcited by 678
  11. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  12. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
20 more
  1. 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
  2. 2021A Field Guide to Federated Optimizationcited by 312
  3. 2021Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learningcited by 192
  4. 2021FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropoutcited by 192
  5. 2019On the Convergence of Local Descent Methods in Federated Learningcited by 150
  6. 2021FedScale: Benchmarking Model and System Performance of Federated Learning at Scalecited by 135
  7. 2020Distilled One-Shot Federated Learningcited by 108
  8. 2021Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated Learningcited by 93
  9. 2019Variance Reduced Local SGD with Lower Communication Complexitycited by 80
  10. 2018Adaptive Communication Strategies to Achieve the Best Error-Runtime Trade-off in Local-Update SGDcited by 76
  11. 2021Fast Federated Learning in the Presence of Arbitrary Device Unavailabilitycited by 72
  12. 2020Cost-Effective Federated Learning Designcited by 61
  13. 2021AutoFL: Enabling Heterogeneity-Aware Energy Efficient Federated Learningcited by 39
  14. 2022Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customizationcited by 34
  15. 2022DELTA: Diverse Client Sampling for Fasting Federated Learningcited by 24
  16. 2020Distributed Non-Convex Optimization with Sublinear Speedup under Intermittent Client Availabilitycited by 20
  17. 2019Federated Learning with Additional Mechanisms on Clients to Reduce Communication Costscited by 12
  18. 2022Towards Energy-Aware Federated Learning on Battery-Powered Clientscited by 11
  19. 2019Faster Distributed Deep Net Training: Computation and Communication Decoupled Stochastic Gradient Descentcited by 7
  20. 2022FedCos: A Scene-adaptive Federated Optimization Enhancement for Performance Improvementcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026FedSLoP: Memory-Efficient Federated Learning with Low-Rank Gradient Projectioncited by 1
  2. 2026FedLLM: A Privacy-Preserving Federated Large Language Model for Explainable Traffic Flow Predictioncited by 0