Federated Learning (FL) recently emerged as a practical privacy-preserving paradigm to exploit data distributed over separated repositories for Machine Learning purposes, with no need to migrate data. FL algorithms entail concerted activities of multiple distributed players: a dedicated supporting system aims to relieve programmers from dealing with the intricate implementation details of communication and synchronization activities required along the distributed model learning, and the necessary information exchange during operation. Such support plays a crucial role in the experimentation of FL algorithms and their eventual field operation, so its architecture must be carefully designed. In this work, we propose a novel architecture where the pivotal role is assigned to a runtime system based on actors, working at the middleware level. The distinctive points of this approach are portability across diverse platforms, location transparency for the involved nodes, opportunity to choose diverse languages for implementing the core parts of custom software systems. Moreover, with the proposed solution, scalability requirements can be easily met. The implementation of FL algorithms is made easier by APIs to programmatically access the middleware functionalities. Another benefit is that the same code can be used in both simulated and Fed-lang, the reference implementation of the proposed architecture, has been used to quantitatively compare the characteristics of our approach with other existing FL frameworks, showing its ability to address the challenges posed by various operating conditions and settings. The described architecture has shown to be adequate to deliver the functionalities required for the effective development of FL systems.

中文翻译：

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为联合学习实验和系统设计基于执行组件的中间件支持


联邦学习 （FL） 最近成为一种实用的隐私保护范式，可利用分布在单独存储库中的数据进行机器学习，而无需迁移数据。联邦学习算法需要多个分布式参与者的协同活动：专用的支持系统旨在将程序员从处理分布式模型学习过程中所需的通信和同步活动的复杂实现细节以及操作过程中的必要信息交换中解放出来。这种支持在 FL 算法的实验及其最终的现场操作中起着至关重要的作用，因此必须仔细设计其架构。在这项工作中，我们提出了一种新颖的架构，其中关键角色被分配给基于 actors 的运行时系统，在中间件级别工作。这种方法的独特之处在于跨不同平台的可移植性、相关节点的位置透明性、选择不同语言来实现自定义软件系统核心部分的机会。此外，使用建议的解决方案，可以轻松满足可扩展性要求。API 以编程方式访问中间件功能，使 FL 算法的实现变得更加容易。另一个好处是，相同的代码可以在模拟和 Fed-lang 中使用，所提议架构的参考实现已被用于定量比较我们的方法与其他现有 FL 框架的特性，表明它能够解决各种操作条件和设置带来的挑战。所描述的架构已被证明足以提供有效开发 FL 系统所需的功能。