Since its debut in 2016, Federated Learning (FL) has been tied to the inner workings of Deep Neural Networks (DNNs). On the one hand, this allowed its development and widespread use as DNNs proliferated. On the other hand, it neglected all those scenarios in which using DNNs is not possible or advantageous. The fact that most current FL frameworks only allow training DNNs reinforces this problem. To address the lack of FL solutions for non-DNN-based use cases, we propose MAFL (Model-Agnostic Federated Learning). MAFL marries a model-agnostic FL algorithm, AdaBoost.F, with an open industry-grade FL framework: Intel OpenFL. MAFL is the first FL system not tied to any specific type of machine learning model, allowing exploration of FL scenarios beyond DNNs and trees. We test MAFL from multiple points of view, assessing its correctness, flexibility and scaling properties up to 64 nodes. We optimised the base software achieving a 5.5x speedup on a standard FL scenario. MAFL is compatible with x86-64, ARM-v8, Power and RISC-V.

翻译：自2016年问世以来，联邦学习（FL）一直与深度神经网络（DNN）的内部运作紧密关联。一方面，这使其随着DNN的普及得以发展并被广泛应用；另一方面，却也忽视了所有无法或不适合使用DNN的场景。目前大多数FL框架仅支持DNN训练的事实进一步加剧了这一问题。为解决非DNN应用场景中FL方案的缺失，我们提出了MAFL（模型无关联邦学习）。MAFL将模型无关的FL算法AdaBoost.F与工业级开源FL框架Intel OpenFL相结合。MAFL是首个不绑定任何特定类型机器学习模型的FL系统，支持探索超越DNN和决策树的FL应用场景。我们从多角度对MAFL进行测试，评估其在64节点环境下的正确性、灵活性与扩展特性。通过优化基础软件，我们在标准FL场景中实现了5.5倍加速。MAFL兼容x86-64、ARM-v8、Power及RISC-V架构。