mlsanityData to 14 Oct 2026

FedADC: Accelerated Federated Learning with Drift Control

arXiv 2012.09102 ↗First posted 16 Dec 2020cs.LG cs.DC cs.ITcited by 26

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Abstract (arXiv)

Federated learning (FL) has become de facto framework for collaborative learning among edge devices with privacy concern. The core of the FL strategy is the use of stochastic gradient descent (SGD) in a distributed manner. Large scale implementation of FL brings new challenges, such as the incorporation of acceleration techniques designed for SGD into the distributed setting, and mitigation of the drift problem due to non-homogeneous distribution of local datasets. These two problems have been separately studied in the literature; whereas, in this paper, we show that it is possible to address both problems using a single strategy without any major alteration to the FL framework, or introducing additional computation and communication load. To achieve this goal, we propose FedADC, which is an accelerated FL algorithm with drift control. We empirically illustrate the advantages of FedADC.

Key facts. Cited by 26 papers in the map (data through 14 Oct 2026). First posted on arXiv 16 Dec 2020.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 9; 2024: 4; 2025: 2; 2026: 2.

26 papers.

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  1. 2023FedSpeed: Larger Local Interval, Less Communication Round, and Higher Generalization Accuracycited by 49
  2. 2023Dynamic Regularized Sharpness Aware Minimization in Federated Learning: Approaching Global Consistency and Smooth Landscapecited by 46
  3. 2021Time-Correlated Sparsification for Communication-Efficient Federated Learningcited by 26
  4. 2022Communication-Efficient Federated Learning with Accelerated Client Gradientcited by 24
  5. 2022Accelerated Federated Learning with Decoupled Adaptive Optimizationcited by 23
  6. 2023Efficient Federated Learning via Local Adaptive Amended Optimizer with Linear Speedupcited by 23
  7. 2023Understanding How Consistency Works in Federated Learning via Stage-wise Relaxed Initializationcited by 21
  8. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  9. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  10. 2024Accelerating Heterogeneous Federated Learning with Closed-form Classifierscited by 10
  11. 2023Window-based Model Averaging Improves Generalization in Heterogeneous Federated Learningcited by 7
  12. 2022Hierarchical Federated Learning with Momentum Acceleration in Multi-Tier Networkscited by 6
14 more
  1. 2021Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regressioncited by 5
  2. 2023FilFL: Client Filtering for Optimized Client Participation in Federated Learningcited by 5
  3. 2023Stabilizing and Improving Federated Learning with Non-IID Data and Client Dropoutcited by 3
  4. 2023Communication-Efficient Heterogeneous Federated Learning with Generalized Heavy-Ball Momentumcited by 3
  5. 2024Momentum Approximation in Asynchronous Private Federated Learningcited by 3
  6. 2022Federated Spatial Reuse Optimization in Next-Generation Decentralized IEEE 802.11 WLANscited by 2
  7. 2022Federated Learning for Non-IID Data via Client Variance Reduction and Adaptive Server Updatecited by 2
  8. 2023Asynchronous Online Federated Learning with Reduced Communication Requirementscited by 2
  9. 2024A-FedPD: Aligning Dual-Drift is All Federated Primal-Dual Learning Needscited by 2
  10. 2022Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processingcited by 0
  11. 2024Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated Learningcited by 0
  12. 2025On the Limits of Momentum in Decentralized and Federated Optimizationcited by 0
  13. 2026Rethinking the Personalized Relaxed Initialization in the Federated Learning: Consistency and Generalizationcited by 0
  14. 2026FedACT: Federated Adaptive Coordinate Trust Modulation for Robust Transformer Training under Data Heterogeneitycited by 0