mlsanityData to 7 Oct 2026

FLeet: Online Federated Learning via Staleness Awareness and Performance Prediction

arXiv 2006.07273 ↗First posted 12 Jun 2020cs.LG cs.DC stat.MLcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 6; 2023: 4; 2024: 3; 2025: 1; 2026: 1.

21 papers.

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  1. 2022Client Selection in Federated Learning: Principles, Challenges, and Opportunitiescited by 62
  2. 2021Federated Neural Collaborative Filteringcited by 30
  3. 2021Resource-Efficient Federated Learningcited by 26
  4. 2023HiFlash: Communication-Efficient Hierarchical Federated Learning with Adaptive Staleness Control and Heterogeneity-aware Client-Edge Associationcited by 17
  5. 2023Federated Learning for Data Streamscited by 15
  6. 2021Personalized Semi-Supervised Federated Learning for Human Activity Recognitioncited by 9
  7. 2024Personalized Federated Learning with Mixture of Models for Adaptive Prediction and Model Fine-Tuningcited by 8
  8. 2022Addressing modern and practical challenges in machine learning: A survey of online federated and transfer learningcited by 6
  9. 2024Energy-Efficient Federated Edge Learning with Streaming Data: A Lyapunov Optimization Approachcited by 6
  10. 2021Bristle: Decentralized Federated Learning in Byzantine, Non-i.i.d. Environmentscited by 4
  11. 2022Graph Federated Learning for CIoT Devices in Smart Home Applicationscited by 4
  12. 2022Latency Aware Semi-synchronous Client Selection and Model Aggregation for Wireless Federated Learningcited by 3
9 more
  1. 2023Dynamic Scheduling for Federated Edge Learning with Streaming Datacited by 3
  2. 2021Location Leakage in Federated Signal Mapscited by 2
  3. 2022FedLesScan: Mitigating Stragglers in Serverless Federated Learningcited by 2
  4. 2022Scheduling Algorithms for Federated Learning with Minimal Energy Consumptioncited by 1
  5. 2023Decentralized Personalized Online Federated Learningcited by 1
  6. 2024Budgeted Online Model Selection and Fine-Tuning via Federated Learningcited by 1
  7. 2025DaringFed: A Dynamic Bayesian Persuasion Pricing for Online Federated Learning under Two-sided Incomplete Informationcited by 1
  8. 2021Towards General-purpose Infrastructure for Protecting Scientific Data Under Studycited by 0
  9. 2026FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Trainingcited by 0