# Dynamic Federated Learning

arXiv: https://arxiv.org/abs/2002.08782 · HTML: https://mlsanity.com/paper/2002.08782/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 12 of 12)

- [Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networks](https://mlsanity.com/paper/2202.02947/) (2022)
- [Performative Federated Learning: A Solution to Model-Dependent and Heterogeneous Distribution Shifts](https://mlsanity.com/paper/2305.05090/) (2023)
- [Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Point](https://mlsanity.com/paper/2203.13950/) (2022)
- [A General Theory for Client Sampling in Federated Learning](https://mlsanity.com/paper/2107.12211/) (2021)
- [CFedAvg: Achieving Efficient Communication and Fast Convergence in Non-IID Federated Learning](https://mlsanity.com/paper/2106.07155/) (2021)
- [Learnings from Federated Learning in the Real world](https://mlsanity.com/paper/2202.03925/) (2022)
- [Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization](https://mlsanity.com/paper/2305.13503/) (2023)
- [DeceFL: A Principled Decentralized Federated Learning Framework](https://mlsanity.com/paper/2107.07171/) (2021)
- [STSyn: Speeding Up Local SGD with Straggler-Tolerant Synchronization](https://mlsanity.com/paper/2210.03521/) (2022)
- [Integrating Personalized Federated Learning with Control Systems for Enhanced Performance](https://mlsanity.com/paper/2501.15728/) (2025)
- [Meta-Federated Learning: A Novel Approach for Real-Time Traffic Flow Management](https://mlsanity.com/paper/2501.16758/) (2025)
- [Online Decentralized Federated Multi-task Learning With Trustworthiness in Cyber-Physical Systems](https://mlsanity.com/paper/2509.00992/) (2025)

## Abstract (arXiv)

Federated learning has emerged as an umbrella term for centralized coordination strategies in multi-agent environments. While many federated learning architectures process data in an online manner, and are hence adaptive by nature, most performance analyses assume static optimization problems and offer no guarantees in the presence of drifts in the problem solution or data characteristics. We consider a federated learning model where at every iteration, a random subset of available agents perform local updates based on their data. Under a non-stationary random walk model on the true minimizer for the aggregate optimization problem, we establish that the performance of the architecture is determined by three factors, namely, the data variability at each agent, the model variability across all agents, and a tracking term that is inversely proportional to the learning rate of the algorithm. The results clarify the trade-off between convergence and tracking performance.