# Federated Learning for Data Streams

arXiv: https://arxiv.org/abs/2301.01542 · HTML: https://mlsanity.com/paper/2301.01542/

## 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 (most cited first, 26 of 26)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [LEAF: A Benchmark for Federated Settings](https://mlsanity.com/paper/1812.01097/) (2018)
- [Federated Learning with Matched Averaging](https://mlsanity.com/paper/2002.06440/) (2020)
- [Federated Learning with Differential Privacy: Algorithms and Performance Analysis](https://mlsanity.com/paper/1911.00222/) (2019)
- [Progress & Compress: A scalable framework for continual learning](https://mlsanity.com/paper/1805.06370/) (2018)
- [Agnostic Federated Learning](https://mlsanity.com/paper/1902.00146/) (2019)
- [Three Approaches for Personalization with Applications to Federated Learning](https://mlsanity.com/paper/2002.10619/) (2020)
- [A Field Guide to Federated Optimization](https://mlsanity.com/paper/2107.06917/) (2021)
- [Applied Federated Learning: Improving Google Keyboard Query Suggestions](https://mlsanity.com/paper/1812.02903/) (2018)
- [Federated Continual Learning with Weighted Inter-client Transfer](https://mlsanity.com/paper/2003.03196/) (2020)
- [Personalized Federated Learning through Local Memorization](https://mlsanity.com/paper/2111.09360/) (2021)
- [Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data](https://mlsanity.com/paper/1810.08553/) (2018)
- [Semi-Cyclic Stochastic Gradient Descent](https://mlsanity.com/paper/1904.10120/) (2019)
- [End-to-End Speech Recognition from Federated Acoustic Models](https://mlsanity.com/paper/2104.14297/) (2021)
- [Network-Aware Optimization of Distributed Learning for Fog Computing](https://mlsanity.com/paper/2004.08488/) (2020)
- [Towards Federated Learning on Time-Evolving Heterogeneous Data](https://mlsanity.com/paper/2112.13246/) (2021)
- [FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction](https://mlsanity.com/paper/2006.07273/) (2020)
- [Distributed Optimization over Block-Cyclic Data](https://mlsanity.com/paper/2002.07454/) (2020)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)

## Built on by (most cited first, 16 of 16)

- [Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning](https://mlsanity.com/paper/2410.21547/) (2024)
- [Unbiased and Sign Compression in Distributed Learning: Comparing Noise Resilience via SDEs](https://mlsanity.com/paper/2502.17009/) (2025)
- [Age Aware Scheduling for Differentially-Private Federated Learning](https://mlsanity.com/paper/2405.05962/) (2024)
- [Sparse Uncertainty-Informed Sampling from Federated Streaming Data](https://mlsanity.com/paper/2408.17108/) (2024)
- [FedCGD: Collective Gradient Divergence Optimized Scheduling for Wireless Federated Learning](https://mlsanity.com/paper/2506.07581/) (2025)
- [Harnessing the Power of Federated Learning in Federated Contextual Bandits](https://mlsanity.com/paper/2312.16341/) (2023)
- [Federated Learning for Collaborative Inference Systems: The Case of Early Exit Networks](https://mlsanity.com/paper/2405.04249/) (2024)
- [Blockchain-Empowered Cyber-Secure Federated Learning for Trustworthy Edge Computing](https://mlsanity.com/paper/2412.20674/) (2024)
- [CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection](https://mlsanity.com/paper/2504.01882/) (2025)
- [Federated Spectral Graph Transformers Meet Neural Ordinary Differential Equations for Non-IID Graphs](https://mlsanity.com/paper/2504.11808/) (2025)
- [FlexFed: Mitigating Catastrophic Forgetting in Heterogeneous Federated Learning in Pervasive Computing Environments](https://mlsanity.com/paper/2505.13576/) (2025)
- [Shift Happens: Mixture of Experts based Continual Adaptation in Federated Learning](https://mlsanity.com/paper/2506.18789/) (2025)
- [Decentralized Federated Learning of Probabilistic Generative Classifiers](https://mlsanity.com/paper/2507.17285/) (2025)
- [Server-Proximal Aggregation for Federated Domain-Incremental Learning under Partial Participation: Task-Uniform Convergence and Backward Transfer](https://mlsanity.com/paper/2601.22274/) (2026)
- [Distributed Optimization with Streaming Data: A Temporal Weighting Perspective](https://mlsanity.com/paper/2608.09565/) (2026)
- [Beyond Aggregate Drift: Optimal Client-Specific Windows in Federated Learning](https://mlsanity.com/paper/2610.15543/) (2026)

## 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.