# Degree of Staleness-Aware Data Updating in Federated Learning

arXiv: https://arxiv.org/abs/2508.16931 · HTML: https://mlsanity.com/paper/2508.16931/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 32 references that are in the map.
- First posted on arXiv 23 Aug 2025.

## Builds on (most cited first, 12 of 12)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Federated Learning Based on Dynamic Regularization](https://mlsanity.com/paper/2111.04263/) (2021)
- [Toward an Automated Auction Framework for Wireless Federated Learning Services Market](https://mlsanity.com/paper/1912.06370/) (2019)
- [Differentially Private Federated Bayesian Optimization with Distributed Exploration](https://mlsanity.com/paper/2110.14153/) (2021)
- [Federated Learning on the Road: Autonomous Controller Design for Connected and Autonomous Vehicles](https://mlsanity.com/paper/2102.03401/) (2021)
- [Communication-Efficient Massive UAV Online Path Control: Federated Learning Meets Mean-Field Game Theory](https://mlsanity.com/paper/2003.04451/) (2020)
- [Enabling Long-Term Cooperation in Cross-Silo Federated Learning: A Repeated Game Perspective](https://mlsanity.com/paper/2106.11814/) (2021)
- [Dynamic Pricing and Mean Field Analysis for Controlling Age of Information](https://mlsanity.com/paper/2004.10050/) (2020)
- [Age Debt: A General Framework For Minimizing Age of Information](https://mlsanity.com/paper/2101.10225/) (2021)
- [Taming Time-Varying Information Asymmetry in Fresh Status Acquisition](https://mlsanity.com/paper/2103.12334/) (2021)

## Abstract (arXiv)

Handling data staleness remains a significant challenge in federated learning with highly time-sensitive tasks, where data is generated continuously and data staleness largely affects model performance. Although recent works attempt to optimize data staleness by determining local data update frequency or client selection strategy, none of them explore taking both data staleness and data volume into consideration. In this paper, we propose DUFL(Data Updating in Federated Learning), an incentive mechanism featuring an innovative local data update scheme manipulated by three knobs: the server's payment, outdated data conservation rate, and clients' fresh data collection volume, to coordinate staleness and volume of local data for best utilities. To this end, we introduce a novel metric called DoS(the Degree of Staleness) to quantify data staleness and conduct a theoretic analysis illustrating the quantitative relationship between DoS and model performance. We model DUFL as a two-stage Stackelberg game with dynamic constraint, deriving the optimal local data update strategy for each client in closed-form and the approximately optimal strategy for the server. Experimental results on real-world datasets demonstrate the significant performance of our approach.