mlsanityData to 7 Oct 2026

FLSys: Toward an Open Ecosystem for Federated Learning Mobile Apps

arXiv 2111.09445 ↗First posted 17 Nov 2021cs.LG cs.AI cs.DCcited by 6

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Abstract (arXiv)

This article presents the design, implementation, and evaluation of FLSys, a mobile-cloud federated learning (FL) system, which can be a key component for an open ecosystem of FL models and apps. FLSys is designed to work on smart phones with mobile sensing data. It balances model performance with resource consumption, tolerates communication failures, and achieves scalability. In FLSys, different DL models with different FL aggregation methods can be trained and accessed concurrently by different apps. Furthermore, FLSys provides advanced privacy preserving mechanisms and a common API for third-party app developers to access FL models. FLSys adopts a modular design and is implemented in Android and AWS cloud. We co-designed FLSys with a human activity recognition (HAR) model. HAR sensing data was collected in the wild from 100+ college students during a 4-month period. We implemented HAR-Wild, a CNN model tailored to mobile devices, with a data augmentation mechanism to mitigate the problem of non-Independent and Identically Distributed data. A sentiment analysis model is also used to demonstrate that FLSys effectively supports concurrent models. This article reports our experience and lessons learned from conducting extensive experiments using simulations, Android/Linux emulations, and Android phones that demonstrate FLSys achieves good model utility and practical system performance.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 36 of its 71 references that are in the map. First posted on arXiv 17 Nov 2021.

Builds on

36 of 71 references.

References · 36

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Cited by year

Citing papers per year: 2023: 3; 2024: 1; 2025: 2.

6 papers.

  1. 2023Zone-based Federated Learning for Mobile Sensing Datacited by 4
  2. 2023Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectivescited by 3
  3. 2024On-device Federated Learning in Smartphones for Detecting Depression from Reddit Postscited by 2
  4. 2023Concept Matching: Clustering-based Federated Continual Learningcited by 1
  5. 2025A Client-level Assessment of Collaborative Backdoor Poisoning in Non-IID Federated Learningcited by 0
  6. 2025Theoretically Unmasking Inference Attacks Against LDP-Protected Clients in Federated Vision Modelscited by 0