mlsanityData to 7 Oct 2026

FedDRL: Deep Reinforcement Learning-based Adaptive Aggregation for Non-IID Data in Federated Learning

arXiv 2208.02442 ↗First posted 4 Aug 2022cs.LG cs.CVcited by 7

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Abstract (arXiv)

The uneven distribution of local data across different edge devices (clients) results in slow model training and accuracy reduction in federated learning. Naive federated learning (FL) strategy and most alternative solutions attempted to achieve more fairness by weighted aggregating deep learning models across clients. This work introduces a novel non-IID type encountered in real-world datasets, namely cluster-skew, in which groups of clients have local data with similar distributions, causing the global model to converge to an over-fitted solution. To deal with non-IID data, particularly the cluster-skewed data, we propose FedDRL, a novel FL model that employs deep reinforcement learning to adaptively determine each client's impact factor (which will be used as the weights in the aggregation process). Extensive experiments on a suite of federated datasets confirm that the proposed FedDRL improves favorably against FedAvg and FedProx methods, e.g., up to 4.05% and 2.17% on average for the CIFAR-100 dataset, respectively.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 4 Aug 2022.

Builds on

20 of 32 references.

References · 20

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2017Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithmscited by 4,283
  4. 2015Continuous control with deep reinforcement learningcited by 3,279
  5. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  6. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  7. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 565
  9. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  10. 2021Federated Learning on Non-IID Data: A Surveycited by 345
  11. 2019The Non-IID Data Quagmire of Decentralized Machine Learningcited by 303
  12. 2020Client Selection in Federated Learning: Convergence Analysis and Power-of-Choice Selection Strategiescited by 196
8 more
  1. 2018Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysiscited by 154
  2. 2020Adaptive Gradient Sparsification for Efficient Federated Learning: An Online Learning Approachcited by 80
  3. 2020Fast-Convergent Federated Learning with Adaptive Weightingcited by 67
  4. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  5. 2021Federated Learning with Fair Averagingcited by 59
  6. 2019Real-World Image Datasets for Federated Learningcited by 30
  7. 2020Fairness and Accuracy in Federated Learningcited by 27
  8. 2021H-FL: A Hierarchical Communication-Efficient and Privacy-Protected Architecture for Federated Learningcited by 14

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 0; 2026: 3.

7 papers.

  1. 2024FedMAC: Tackling Partial-Modality Missing in Federated Learning with Cross-Modal Aggregation and Contrastive Regularizationcited by 16
  2. 2024A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applicationscited by 8
  3. 2023CADIS: Handling Cluster-skewed Non-IID Data in Federated Learning with Clustered Aggregation and Knowledge DIStilled Regularizationcited by 5
  4. 2026Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Datacited by 3
  5. 2024PARDON: Privacy-Aware and Robust Federated Domain Generalizationcited by 0
  6. 2026Assessing the Impacts of Imperfect Datasets on Client Selections in Federated Learningcited by 0
  7. 2026Cooperative Multi-Agent Reinforcement Learning for Adaptive Aggregation in Semi-Supervised Federated Learning with non-IID Datacited by 0