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.

中文翻译：

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FLaaS：联邦学习即服务

联邦学习 (FL) 正在成为一种有前途的技术，可以以分散的、保护隐私的方式构建机器学习模型。事实上，FL 可以在用户设备上进行本地培训，避免将用户数据传输到中央服务器，并且可以通过差异隐私机制进行增强。尽管 FL 最近已部署在实际系统中，但尚未探索跨不同 3 方应用程序进行协作建模的可能性。在本文中，我们解决了这个问题并提出了联邦学习即服务 (FLaaS)，这是一个支持 3rd 方应用程序协作模型构建的不同场景的系统，并解决了权限和隐私管理、可用性和分层模型训练等随之而来的挑战。FLaaS 可以部署在不同的操作环境中。作为概念证明，我们在手机设置上实现它，并讨论模拟和真实设备上结果的实际影响，涉及设备上训练 CPU 成本、内存占用和每个 FL 模型回合的功耗。因此，我们证明了 FLaaS 在几个小时内跨 100 台设备跨应用程序构建独特或联合 FL 模型以进行图像对象检测的可行性。