# DynamicFL: Balancing Communication Dynamics and Client Manipulation for Federated Learning

arXiv: https://arxiv.org/abs/2308.06267 · HTML: https://mlsanity.com/paper/2308.06267/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 16 Jul 2023.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices](https://mlsanity.com/paper/1707.01083/) (2017)
- [Adaptive Federated Optimization](https://mlsanity.com/paper/2003.00295/) (2020)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale](https://mlsanity.com/paper/1811.00982/) (2018)
- [HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients](https://mlsanity.com/paper/2010.01264/) (2020)
- [FedScale: Benchmarking Model and System Performance of Federated Learning at Scale](https://mlsanity.com/paper/2105.11367/) (2021)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [FedBalancer: Data and Pace Control for Efficient Federated Learning on Heterogeneous Clients](https://mlsanity.com/paper/2201.01601/) (2022)
- [Federated Transfer Reinforcement Learning for Autonomous Driving](https://mlsanity.com/paper/1910.06001/) (2019)
- [Understanding Multi-Turn Toxic Behaviors in Open-Domain Chatbots](https://mlsanity.com/paper/2307.09579/) (2023)
- [VSMask: Defending Against Voice Synthesis Attack via Real-Time Predictive Perturbation](https://mlsanity.com/paper/2305.05736/) (2023)
- [On Addressing Heterogeneity in Federated Learning for Autonomous Vehicles Connected to a Drone Orchestrator](https://mlsanity.com/paper/2108.02712/) (2021)
- [Security Defense For Smart Contracts: A Comprehensive Survey](https://mlsanity.com/paper/2302.07347/) (2023)

## Built on by (most cited first, 1 of 1)

- [Hydra: Accurate Multi-Modal Leaf Wetness Sensing with mm-Wave and Camera Fusion](https://mlsanity.com/paper/2508.02409/) (2025)

## Abstract (arXiv)

Federated Learning (FL) is a distributed machine learning (ML) paradigm, aiming to train a global model by exploiting the decentralized data across millions of edge devices. Compared with centralized learning, FL preserves the clients' privacy by refraining from explicitly downloading their data. However, given the geo-distributed edge devices (e.g., mobile, car, train, or subway) with highly dynamic networks in the wild, aggregating all the model updates from those participating devices will result in inevitable long-tail delays in FL. This will significantly degrade the efficiency of the training process. To resolve the high system heterogeneity in time-sensitive FL scenarios, we propose a novel FL framework, DynamicFL, by considering the communication dynamics and data quality across massive edge devices with a specially designed client manipulation strategy. ours actively selects clients for model updating based on the network prediction from its dynamic network conditions and the quality of its training data. Additionally, our long-term greedy strategy in client selection tackles the problem of system performance degradation caused by short-term scheduling in a dynamic network. Lastly, to balance the trade-off between client performance evaluation and client manipulation granularity, we dynamically adjust the length of the observation window in the training process to optimize the long-term system efficiency. Compared with the state-of-the-art client selection scheme in FL, ours can achieve a better model accuracy while consuming only 18.9% -- 84.0% of the wall-clock time. Our component-wise and sensitivity studies further demonstrate the robustness of ours under various real-life scenarios.