mlsanityData to 14 Oct 2026

Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift

arXiv 2211.12578 ↗First posted 22 Nov 2022cs.LG cs.AI cs.SYcited by 10

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Abstract (arXiv)

Federated Learning (FL) is an emerging domain in the broader context of artificial intelligence research. Methodologies pertaining to FL assume distributed model training, consisting of a collection of clients and a server, with the main goal of achieving optimal global model with restrictions on data sharing due to privacy concerns. It is worth highlighting that the diverse existing literature in FL mostly assume stationary data generation processes; such an assumption is unrealistic in real-world conditions where concept drift occurs due to, for instance, seasonal or period observations, faults in sensor measurements. In this paper, we introduce a multiscale algorithmic framework which combines theoretical guarantees of textitFedAvg and textitFedOMD algorithms in near stationary settings with a non-stationary detection and adaptation technique to ameliorate FL generalization performance in the presence of concept drifts. We present a multi-scale algorithmic framework leading to TildeO ( min √LT , Delta^frac13T^frac23 + √T ) textitdynamic regret for T rounds with an underlying general convex loss function, where L is the number of times non-stationary drifts occurred and Delta is the cumulative magnitude of drift experienced within T rounds.

Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 36 of its 65 references that are in the map. First posted on arXiv 22 Nov 2022.

Builds on

36 of 65 references.

References · 36

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  3. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  4. 2020Adaptive Federated Optimizationcited by 1,046
  5. 2020Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimizationcited by 906
  6. 2019Introduction to Online Convex Optimizationcited by 803
  7. 2018Local SGD Converges Fast and Communicates Littlecited by 605
  8. 2021FedBN: Federated Learning on Non-IID Features via Local Batch Normalizationcited by 571
  9. 2019Asynchronous Federated Optimizationcited by 267
  10. 2021Federated Learning for Internet of Things: A Comprehensive Surveycited by 252
  11. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  12. 2020FedBE: Making Bayesian Model Ensemble Applicable to Federated Learningcited by 176
24 more
  1. 2019SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentumcited by 121
  2. 2015Online Optimization : Competing with Dynamic Comparatorscited by 99
  3. 2015Global Convergence of ADMM in Nonconvex Nonsmooth Optimizationcited by 98
  4. 2021Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approachcited by 98
  5. 2020TiFL: A Tier-based Federated Learning Systemcited by 96
  6. 2019Communication-efficient distributed SGD with Sketchingcited by 81
  7. 2020Faster Non-Convex Federated Learning via Global and Local Momentumcited by 48
  8. 2016Improved Dynamic Regret for Non-degenerate Functionscited by 46
  9. 2021Cluster-driven Graph Federated Learning over Multiple Domainscited by 44
  10. 2016Distributed Online Optimization in Dynamic Environments Using Mirror Descentcited by 32
  11. 2021Concept drift detection and adaptation for federated and continual learningcited by 32
  12. 2021A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data Streamscited by 28
  13. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  14. 2021Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networkscited by 19
  15. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  16. 2021PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streamscited by 17
  17. 2021Asynchronous Federated Learning for Sensor Data with Concept Driftcited by 14
  18. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  19. 2020Distributed Online Convex Optimization with an Aggregative Variablecited by 7
  20. 2021Improving Dynamic Regret in Distributed Online Mirror Descent Using Primal and Dual Informationcited by 7
  21. 2019Online Learning over Dynamic Graphs via Distributed Proximal Gradient Algorithmcited by 5
  22. 2019Distributed Online Convex Optimization with Improved Dynamic Regretcited by 4
  23. 2021Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networkscited by 3
  24. 2021Distribution Shift in Airline Customer Behavior during COVID-19cited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 4; 2025: 2; 2026: 4.

10 papers.

  1. 2025Federated Continual Learning: Concepts, Challenges, and Solutionscited by 13
  2. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  3. 2024Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selectioncited by 3
  4. 2024Federated Learning Clients Clustering with Adaptation to Data Driftscited by 3
  5. 2026Achieving Optimal Static and Dynamic Regret Simultaneously in Bandits with Deterministic Lossescited by 1
  6. 2024Efficient Data Distribution Estimation for Accelerated Federated Learningcited by 0
  7. 2025Event-Driven Online Vertical Federated Learningcited by 0
  8. 2026FedSEA: Achieving Benefit of Parallelization in Federated Online Learningcited by 0
  9. 2026Autonomous Drift Learning in Data Streams: A Unified Perspectivecited by 0
  10. 2026Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learningcited by 0