mlsanityData to 7 Oct 2026

Federated Learning under Distributed Concept Drift

arXiv 2206.00799 ↗First posted 1 Jun 2022cs.LGcited by 35

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Abstract (arXiv)

Federated Learning (FL) under distributed concept drift is a largely unexplored area. Although concept drift is itself a well-studied phenomenon, it poses particular challenges for FL, because drifts arise staggered in time and space (across clients). To the best of our knowledge, this work is the first to explicitly study data heterogeneity in both dimensions. We first demonstrate that prior solutions to drift adaptation that use a single global model are ill-suited to staggered drifts, necessitating multiple-model solutions. We identify the problem of drift adaptation as a time-varying clustering problem, and we propose two new clustering algorithms for reacting to drifts based on local drift detection and hierarchical clustering. Empirical evaluation shows that our solutions achieve significantly higher accuracy than existing baselines, and are comparable to an idealized algorithm with oracle knowledge of the ground-truth clustering of clients to concepts at each time step.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 24 of its 54 references that are in the map. First posted on arXiv 1 Jun 2022.

Builds on

24 of 54 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  3. 2019Advances and Open Problems in Federated Learningcited by 3,021
  4. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  5. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  6. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,366
  7. 2020The Future of Digital Health with Federated Learningcited by 625
  8. 2020An Efficient Framework for Clustered Federated Learningcited by 505
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2020Learning under Concept Drift: A Reviewcited by 470
  11. 2017Functional Map of the Worldcited by 338
  12. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 321
12 more
  1. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  2. 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
  3. 2019Regularized Learning for Domain Adaptation under Label Shiftscited by 118
  4. 2019Robust Federated Learning in a Heterogeneous Environmentcited by 88
  5. 2020Ekya: Continuous Learning of Video Analytics Models on Edge Compute Serverscited by 58
  6. 2021Flexible Clustered Federated Learning for Client-Level Data Distribution Shiftcited by 47
  7. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  8. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  9. 2021Asynchronous Federated Learning for Sensor Data with Concept Driftcited by 14
  10. 2017McDiarmid Drift Detection Methods for Evolving Data Streamscited by 13
  11. 2021Concept Drift Detection in Federated Networked Systemscited by 10
  12. 2021Distribution Shift in Airline Customer Behavior during COVID-19cited by 3

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 5; 2024: 9; 2025: 11; 2026: 6.

35 papers.

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  1. 2022Vertical Federated Learning: A Structured Literature Reviewcited by 17
  2. 2023Online Boosting Adaptive Learning under Concept Drift for Multistream Classificationcited by 17
  3. 2023FedRC: Tackling Diverse Distribution Shifts Challenge in Federated Learning by Robust Clusteringcited by 15
  4. 2024FOOGD: Federated Collaboration for Both Out-of-distribution Generalization and Detectioncited by 14
  5. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  6. 2025Accurate Forgetting for Heterogeneous Federated Continual Learningcited by 11
  7. 2023Patchwork Learning: A Paradigm Towards Integrative Analysis across Diverse Biomedical Data Sourcescited by 10
  8. 2023On the Local Cache Update Rules in Streaming Federated Learningcited by 8
  9. 2024Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Driftcited by 8
  10. 2024Balancing Similarity and Complementarity for Federated Learningcited by 7
  11. 2022Flow: Per-Instance Personalized Federated Learning Through Dynamic Routingcited by 6
  12. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
23 more
  1. 2025Federated Learning for Diffusion Modelscited by 6
  2. 2022Federated Continual Learning to Detect Accounting Anomalies in Financial Auditingcited by 4
  3. 2022Quantifying the Impact of Label Noise on Federated Learningcited by 3
  4. 2024Adaptive Active Inference Agents for Heterogeneous and Lifelong Federated Learningcited by 3
  5. 2024Federated Learning Clients Clustering with Adaptation to Data Driftscited by 3
  6. 2024Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learningcited by 2
  7. 2025A survey on Clustered Federated Learning: Taxonomy, Analysis and Applicationscited by 2
  8. 2025An Information-Theoretic Analysis for Federated Learning under Concept Driftcited by 2
  9. 2024Friends in Unexpected Places: Enhancing Local Fairness in Federated Learning through Clusteringcited by 1
  10. 2025FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learningcited by 1
  11. 2025FedTeddi: Temporal Drift and Divergence Aware Scheduling for Timely Federated Edge Learningcited by 1
  12. 2023Concept-aware clustering for decentralized deep learning under temporal shiftcited by 0
  13. 2024Client2Vec: Improving Federated Learning by Distribution Shifts Aware Client Indexingcited by 0
  14. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  15. 2025Stratify: Rethinking Federated Learning for Non-IID Data through Balanced Samplingcited by 0
  16. 2025Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learningcited by 0
  17. 2025Domain Borders Are There to Be Crossed With Federated Few-Shot Adaptationcited by 0
  18. 2026Federated Concept-Based Models: Interpretable models with distributed supervisioncited by 0
  19. 2026FedDAG: Clustered Federated Learning via Global Data and Gradient Integration for Heterogeneous Environmentscited by 0
  20. 2026DriftGuard: Mitigating Asynchronous Data Drift in Federated Learningcited by 0
  21. 2026Hierarchical Federated Learning with Dynamic Clustering and Adaptive Regularization for Robust Infrastructure Inspectioncited by 0
  22. 2026FedTVD: Balancing Data Quality and Quantity for Robust Federated Learningcited by 0
  23. 2026Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Poolscited by 0