mlsanityData to 14 Oct 2026

Federated Learning for Data Streams

arXiv 2301.01542 ↗First posted 4 Jan 2023cs.LG cs.AI math.OCcited by 16

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Abstract (arXiv)

Federated learning (FL) is an effective solution to train machine learning models on the increasing amount of data generated by IoT devices and smartphones while keeping such data localized. Most previous work on federated learning assumes that clients operate on static datasets collected before training starts. This approach may be inefficient because 1) it ignores new samples clients collect during training, and 2) it may require a potentially long preparatory phase for clients to collect enough data. Moreover, learning on static datasets may be simply impossible in scenarios with small aggregate storage across devices. It is, therefore, necessary to design federated algorithms able to learn from data streams. In this work, we formulate and study the problem of federated learning for data streams. We propose a general FL algorithm to learn from data streams through an opportune weighted empirical risk minimization. Our theoretical analysis provides insights to configure such an algorithm, and we evaluate its performance on a wide range of machine learning tasks.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 26 of its 55 references that are in the map. First posted on arXiv 4 Jan 2023.

Builds on

26 of 55 references.

References · 26

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  2. 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
  3. 2018Federated Optimization in Heterogeneous Networkscited by 2,987
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,229
  6. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,150
  7. 2020Adaptive Federated Optimizationcited by 1,046
  8. 2019Introduction to Online Convex Optimizationcited by 803
  9. 2018LEAF: A Benchmark for Federated Settingscited by 792
  10. 2020Federated Learning with Matched Averagingcited by 564
  11. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 536
  12. 2018Progress & Compress: A scalable framework for continual learningcited by 520
14 more
  1. 2019Agnostic Federated Learningcited by 456
  2. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  3. 2021A Field Guide to Federated Optimizationcited by 315
  4. 2018Applied Federated Learning: Improving Google Keyboard Query Suggestionscited by 204
  5. 2020Federated Continual Learning with Weighted Inter-client Transfercited by 161
  6. 2021Personalized Federated Learning through Local Memorizationcited by 57
  7. 2018Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Datacited by 46
  8. 2019Semi-Cyclic Stochastic Gradient Descentcited by 40
  9. 2021End-to-End Speech Recognition from Federated Acoustic Modelscited by 30
  10. 2020Network-Aware Optimization of Distributed Learning for Fog Computingcited by 27
  11. 2021Towards Federated Learning on Time-Evolving Heterogeneous Datacited by 23
  12. 2020FLeet: Online Federated Learning via Staleness Awareness and Performance Predictioncited by 21
  13. 2020Distributed Optimization over Block-Cyclic Datacited by 6
  14. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 7; 2026: 3.

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  1. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  2. 2025Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEscited by 5
  3. 2024Age Aware Scheduling for Differentially-Private Federated Learningcited by 2
  4. 2024Sparse Uncertainty-Informed Sampling from Federated Streaming Datacited by 2
  5. 2025FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learningcited by 2
  6. 2023Harnessing the Power of Federated Learning in Federated Contextual Banditscited by 1
  7. 2024Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networkscited by 0
  8. 2024Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computingcited by 0
  9. 2025CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detectioncited by 0
  10. 2025Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphscited by 0
  11. 2025FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environmentscited by 0
  12. 2025Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learningcited by 0
4 more
  1. 2025Decentralized Federated Learning of Probabilistic Generative Classifierscited by 0
  2. 2026Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfercited by 0
  3. 2026Distributed Optimization with Streaming Data: A Temporal Weighting Perspectivecited by 0
  4. 2026Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learningcited by 0