# On the Local Cache Update Rules in Streaming Federated Learning

arXiv: https://arxiv.org/abs/2303.16340 · HTML: https://mlsanity.com/paper/2303.16340/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 39 references that are in the map.
- First posted on arXiv 28 Mar 2023.

## Builds on (most cited first, 17 of 17)

- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Federated Learning: Challenges, Methods, and Future Directions](https://mlsanity.com/paper/1908.07873/) (2019)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Local SGD Converges Fast and Communicates Little](https://mlsanity.com/paper/1805.09767/) (2018)
- [Learning under Concept Drift: A Review](https://mlsanity.com/paper/2004.05785/) (2020)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning](https://mlsanity.com/paper/1807.06629/) (2018)
- [Achieving Linear Speedup with Partial Worker Participation in Non-IID Federated Learning](https://mlsanity.com/paper/2101.11203/) (2021)
- [Cooperative SGD: A unified Framework for the Design and Analysis of Communication-Efficient SGD Algorithms](https://mlsanity.com/paper/1808.07576/) (2018)
- [FedVARP: Tackling the Variance Due to Partial Client Participation in Federated Learning](https://mlsanity.com/paper/2207.14130/) (2022)
- [Anarchic Federated Learning](https://mlsanity.com/paper/2108.09875/) (2021)
- [Federated Learning under Distributed Concept Drift](https://mlsanity.com/paper/2206.00799/) (2022)
- [Concept drift detection and adaptation for federated and continual learning](https://mlsanity.com/paper/2105.13309/) (2021)
- [Robust Smartphone App Identification Via Encrypted Network Traffic Analysis](https://mlsanity.com/paper/1704.06099/) (2017)
- [Autonomous Unknown-Application Filtering and Labeling for DL-based Traffic Classifier Update](https://mlsanity.com/paper/2002.06359/) (2020)
- [Asynchronous Federated Learning for Sensor Data with Concept Drift](https://mlsanity.com/paper/2109.00151/) (2021)
- [A Federated Semi-Supervised Learning Approach for Network Traffic Classification](https://mlsanity.com/paper/2107.03933/) (2021)

## Built on by (most cited first, 8 of 8)

- [Federated Continual Learning: Concepts, Challenges, and Solutions](https://mlsanity.com/paper/2502.07059/) (2025)
- [Online Vertical Federated Learning for Cooperative Spectrum Sensing](https://mlsanity.com/paper/2312.11363/) (2023)
- [Multimodal Online Federated Learning with Modality Missing in Internet of Things](https://mlsanity.com/paper/2505.16138/) (2025)
- [Mitigating Modality Quantity and Quality Imbalance in Multimodal Online Federated Learning](https://mlsanity.com/paper/2508.11159/) (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)
- [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)
- [Joint Optimization of Training and Inference in Federated Edge Learning via Constrained Multi-Objective Deep Reinforcement Learning](https://mlsanity.com/paper/2605.25916/) (2026)

## Abstract (arXiv)

In this study, we address the emerging field of Streaming Federated Learning (SFL) and propose local cache update rules to manage dynamic data distributions and limited cache capacity. Traditional federated learning relies on fixed data sets, whereas in SFL, data is streamed, and its distribution changes over time, leading to discrepancies between the local training dataset and long-term distribution. To mitigate this problem, we propose three local cache update rules - First-In-First-Out (FIFO), Static Ratio Selective Replacement (SRSR), and Dynamic Ratio Selective Replacement (DRSR) - that update the local cache of each client while considering the limited cache capacity. Furthermore, we derive a convergence bound for our proposed SFL algorithm as a function of the distribution discrepancy between the long-term data distribution and the client's local training dataset. We then evaluate our proposed algorithm on two datasets: a network traffic classification dataset and an image classification dataset. Our experimental results demonstrate that our proposed local cache update rules significantly reduce the distribution discrepancy and outperform the baseline methods. Our study advances the field of SFL and provides practical cache management solutions in federated learning.