# Concept drift detection and adaptation for federated and continual learning

arXiv: https://arxiv.org/abs/2105.13309 · HTML: https://mlsanity.com/paper/2105.13309/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 33 references that are in the map.
- First posted on arXiv 27 May 2021.

## Builds on (most cited first, 16 of 16)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Federated Learning with Non-IID Data](https://mlsanity.com/paper/1806.00582/) (2018)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Three scenarios for continual learning](https://mlsanity.com/paper/1904.07734/) (2019)
- [Federated Learning for Mobile Keyboard Prediction](https://mlsanity.com/paper/1811.03604/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Adaptive Personalized Federated Learning](https://mlsanity.com/paper/2003.13461/) (2020)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [Characterizing Concept Drift](https://mlsanity.com/paper/1511.03816/) (2015)
- [Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges](https://mlsanity.com/paper/1907.00182/) (2019)
- [Federated and continual learning for classification tasks in a society of devices](https://mlsanity.com/paper/2006.07129/) (2020)

## Built on by (most cited first, 30 of 32)

- [TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation](https://mlsanity.com/paper/2303.06937/) (2023)
- [Non-IID data and Continual Learning processes in Federated Learning: A long road ahead](https://mlsanity.com/paper/2111.13394/) (2021)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Towards Federated Learning on Time-Evolving Heterogeneous Data](https://mlsanity.com/paper/2112.13246/) (2021)
- [Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts](https://mlsanity.com/paper/2305.05090/) (2023)
- [FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imaging](https://mlsanity.com/paper/2109.09658/) (2021)
- [Fed-CPrompt: Contrastive Prompt for Rehearsal-Free Federated Continual Learning](https://mlsanity.com/paper/2307.04869/) (2023)
- [Tackling Dynamics in Federated Incremental Learning with Variational Embedding Rehearsal](https://mlsanity.com/paper/2110.09695/) (2021)
- [Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Drift](https://mlsanity.com/paper/2211.12578/) (2022)
- [On the Local Cache Update Rules in Streaming Federated Learning](https://mlsanity.com/paper/2303.16340/) (2023)
- [Classifier Clustering and Feature Alignment for Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2410.18478/) (2024)
- [Federated Continual Learning for Socially Aware Robotics](https://mlsanity.com/paper/2201.05527/) (2022)
- [Jointly Exploring Client Drift and Catastrophic Forgetting in Dynamic Learning](https://mlsanity.com/paper/2309.00688/) (2023)
- [Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing](https://mlsanity.com/paper/2210.15051/) (2022)
- [On Knowledge Editing in Federated Learning: Perspectives, Challenges, and Future Directions](https://mlsanity.com/paper/2306.01431/) (2023)
- [A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration](https://mlsanity.com/paper/2309.17192/) (2023)
- [Attentive Federated Learning for Concept Drift in Distributed 5G Edge Networks](https://mlsanity.com/paper/2111.07457/) (2021)
- [Federated Learning Clients Clustering with Adaptation to Data Drifts](https://mlsanity.com/paper/2411.01580/) (2024)
- [Machine Learning for QoS Prediction in Vehicular Communication: Challenges and Solution Approaches](https://mlsanity.com/paper/2302.11966/) (2023)
- [Unveiling Group-Specific Distributed Concept Drift: A Fairness Imperative in Federated Learning](https://mlsanity.com/paper/2402.07586/) (2024)
- [FLAME: Adaptive and Reactive Concept Drift Mitigation for Federated Learning Deployments](https://mlsanity.com/paper/2410.01386/) (2024)
- [Holistic Continual Learning under Concept Drift with Adaptive Memory Realignment](https://mlsanity.com/paper/2507.02310/) (2025)
- [Concept Matching: Clustering-based Federated Continual Learning](https://mlsanity.com/paper/2311.06921/) (2023)
- [A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective](https://mlsanity.com/paper/2402.12627/) (2024)
- [FedDAA: Dynamic Client Clustering for Concept Drift Adaptation in Federated Learning](https://mlsanity.com/paper/2506.21054/) (2025)
- [Federated continual learning: A comprehensive survey on lifelong and privacy-preserving learning over distributed and non-stationary data](https://mlsanity.com/paper/2606.11272/) (2026)
- [C2FL: Clustered Continual Federated Learning under Spatial and Temporal Drift](https://mlsanity.com/paper/2606.18003/) (2026)
- [AI Models Close to your Chest: Robust Federated Learning Strategies for Multi-site CT](https://mlsanity.com/paper/2303.13567/) (2023)
- [Towards Privacy-Aware and Personalised Assistive Robots: A User-Centred Approach](https://mlsanity.com/paper/2405.14528/) (2024)
- [Dynamic Allocation Hypernetwork with Adaptive Model Recalibration for FCL](https://mlsanity.com/paper/2503.18064/) (2025)

## Abstract (arXiv)

Smart devices, such as smartphones, wearables, robots, and others, can collect vast amounts of data from their environment. This data is suitable for training machine learning models, which can significantly improve their behavior, and therefore, the user experience. Federated learning is a young and popular framework that allows multiple distributed devices to train deep learning models collaboratively while preserving data privacy. Nevertheless, this approach may not be optimal for scenarios where data distribution is non-identical among the participants or changes over time, causing what is known as concept drift. Little research has yet been done in this field, but this kind of situation is quite frequent in real life and poses new challenges to both continual and federated learning. Therefore, in this work, we present a new method, called Concept-Drift-Aware Federated Averaging (CDA-FedAvg). Our proposal is an extension of the most popular federated algorithm, Federated Averaging (FedAvg), enhancing it for continual adaptation under concept drift. We empirically demonstrate the weaknesses of regular FedAvg and prove that CDA-FedAvg outperforms it in this type of scenario.