# FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

arXiv: https://arxiv.org/abs/2006.07273 · HTML: https://mlsanity.com/paper/2006.07273/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2020.

## Built on by (most cited first, 21 of 21)

- [Client Selection in Federated Learning: Principles, Challenges, and Opportunities](https://mlsanity.com/paper/2211.01549/) (2022)
- [Federated Neural Collaborative Filtering](https://mlsanity.com/paper/2106.04405/) (2021)
- [Resource-Efficient Federated Learning](https://mlsanity.com/paper/2111.01108/) (2021)
- [HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Association](https://mlsanity.com/paper/2301.06447/) (2023)
- [Federated Learning for Data Streams](https://mlsanity.com/paper/2301.01542/) (2023)
- [Personalized Semi-Supervised Federated Learning for Human Activity Recognition](https://mlsanity.com/paper/2104.08094/) (2021)
- [Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuning](https://mlsanity.com/paper/2410.21547/) (2024)
- [Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learning](https://mlsanity.com/paper/2202.03070/) (2022)
- [Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approach](https://mlsanity.com/paper/2405.12046/) (2024)
- [Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environments](https://mlsanity.com/paper/2110.11006/) (2021)
- [Graph Federated Learning for CIoT Devices in Smart Home Applications](https://mlsanity.com/paper/2212.14395/) (2022)
- [Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learning](https://mlsanity.com/paper/2210.10311/) (2022)
- [Dynamic Scheduling for Federated Edge Learning with Streaming Data](https://mlsanity.com/paper/2305.01238/) (2023)
- [Location Leakage in Federated Signal Maps](https://mlsanity.com/paper/2112.03452/) (2021)
- [FedLesScan: Mitigating Stragglers in Serverless Federated Learning](https://mlsanity.com/paper/2211.05739/) (2022)
- [Scheduling Algorithms for Federated Learning with Minimal Energy Consumption](https://mlsanity.com/paper/2209.06210/) (2022)
- [Decentralized Personalized Online Federated Learning](https://mlsanity.com/paper/2311.04817/) (2023)
- [Budgeted Online Model Selection and Fine-Tuning via Federated Learning](https://mlsanity.com/paper/2401.10478/) (2024)
- [DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Information](https://mlsanity.com/paper/2505.05842/) (2025)
- [Towards General-purpose Infrastructure for Protecting Scientific Data Under Study](https://mlsanity.com/paper/2110.01315/) (2021)
- [FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Training](https://mlsanity.com/paper/2605.02125/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is very appealing for its privacy benefits: essentially, a global model is trained with updates computed on mobile devices while keeping the data of users local. Standard FL infrastructures are however designed to have no energy or performance impact on mobile devices, and are therefore not suitable for applications that require frequent (online) model updates, such as news recommenders. This paper presents FLeet, the first Online FL system, acting as a middleware between the Android OS and the machine learning application. FLeet combines the privacy of Standard FL with the precision of online learning thanks to two core components: (i) I-Prof, a new lightweight profiler that predicts and controls the impact of learning tasks on mobile devices, and (ii) AdaSGD, a new adaptive learning algorithm that is resilient to delayed updates. Our extensive evaluation shows that Online FL, as implemented by FLeet, can deliver a 2.3x quality boost compared to Standard FL, while only consuming 0.036% of the battery per day. I-Prof can accurately control the impact of learning tasks by improving the prediction accuracy up to 3.6x (computation time) and up to 19x (energy). AdaSGD outperforms alternative FL approaches by 18.4% in terms of convergence speed on heterogeneous data.