# FLaaS: Federated Learning as a Service

arXiv: https://arxiv.org/abs/2011.09359 · HTML: https://mlsanity.com/paper/2011.09359/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 22 of 22)

- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [Federated Learning: Opportunities and Challenges](https://mlsanity.com/paper/2101.05428/) (2021)
- [Topology-aware Federated Learning in Edge Computing: A Comprehensive Survey](https://mlsanity.com/paper/2302.02573/) (2023)
- [Pervasive AI for IoT applications: A Survey on Resource-efficient Distributed Artificial Intelligence](https://mlsanity.com/paper/2105.01798/) (2021)
- [Auxo: Efficient Federated Learning via Scalable Client Clustering](https://mlsanity.com/paper/2210.16656/) (2022)
- [RBLA: Rank-Based-LoRA-Aggregation for Fine-tuning Heterogeneous Models in FLaaS](https://mlsanity.com/paper/2408.08699/) (2024)
- [Zero-touch realization of Pervasive Artificial Intelligence-as-a-service in 6G networks](https://mlsanity.com/paper/2307.11468/) (2023)
- [The Rise and Fall of Fake News sites: A Traffic Analysis](https://mlsanity.com/paper/2103.09258/) (2021)
- [P4L: Privacy Preserving Peer-to-Peer Learning for Infrastructureless Setups](https://mlsanity.com/paper/2302.13438/) (2023)
- [Towards Open Federated Learning Platforms: Survey and Vision from Technical and Legal Perspectives](https://mlsanity.com/paper/2307.02140/) (2023)
- [DID-eFed: Facilitating Federated Learning as a Service with Decentralized Identities](https://mlsanity.com/paper/2105.08671/) (2021)
- [Poster: Sponge ML Model Attacks of Mobile Apps](https://mlsanity.com/paper/2303.01243/) (2023)
- [Multi-Criteria Client Selection and Scheduling with Fairness Guarantee for Federated Learning Service](https://mlsanity.com/paper/2312.14941/) (2023)
- [Fabricated Flips: Poisoning Federated Learning without Data](https://mlsanity.com/paper/2202.05877/) (2022)
- [Privacy-Preserving Online Content Moderation: A Federated Learning Use Case](https://mlsanity.com/paper/2209.11843/) (2022)
- [DPBalance: Efficient and Fair Privacy Budget Scheduling for Federated Learning as a Service](https://mlsanity.com/paper/2402.09715/) (2024)
- [Where is the Testbed for my Federated Learning Research?](https://mlsanity.com/paper/2407.14154/) (2024)
- [Test-Time Adaptive Composition for Machine Learning as a Service (MLaaS) in IoT Environments](https://mlsanity.com/paper/2606.07685/) (2026)
- [OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence](https://mlsanity.com/paper/2607.22805/) (2026)
- [Designing Sustainable Federated Learning as a Service using Neural Architecture Search](https://mlsanity.com/paper/2608.14359/) (2026)
- [Efficiently Distributed Federated Learning](https://mlsanity.com/paper/2609.19972/) (2026)
- [Distributed Learning as a Service: The Developer's Perspective](https://mlsanity.com/paper/2609.31061/) (2026)

## Abstract (arXiv)

Federated Learning (FL) is emerging as a promising technology to build machine learning models in a decentralized, privacy-preserving fashion. Indeed, FL enables local training on user devices, avoiding user data to be transferred to centralized servers, and can be enhanced with differential privacy mechanisms. Although FL has been recently deployed in real systems, the possibility of collaborative modeling across different 3rd-party applications has not yet been explored. In this paper, we tackle this problem and present Federated Learning as a Service (FLaaS), a system enabling different scenarios of 3rd-party application collaborative model building and addressing the consequent challenges of permission and privacy management, usability, and hierarchical model training. FLaaS can be deployed in different operational environments. As a proof of concept, we implement it on a mobile phone setting and discuss practical implications of results on simulated and real devices with respect to on-device training CPU cost, memory footprint and power consumed per FL model round. Therefore, we demonstrate FLaaS's feasibility in building unique or joint FL models across applications for image object detection in a few hours, across 100 devices.