Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning.

翻译：近年来，人工智能技术的进步使其在商业和工业领域得以成功应用。然而，这些技术需要以集中方式聚合大量数据，从而限制了其在数据敏感或数据传输成本高昂的场景中的适用性。联邦学习通过去中心化模型训练来缓解这些问题，从而消除了数据迁移和聚合的必要性。为推动联邦学习的普及，需要开展更多研发工作以解决若干重要的开放性问题。本文提出 OpenFed，这是一个面向端到端联邦学习的开源软件框架。OpenFed 通过有针对性地消除现有痛点，降低了研究人员和下游用户进入联邦学习领域的门槛。对研究人员而言，OpenFed 提供了一种框架，可在此框架中轻松实现新方法并对照广泛的基准测试集进行公平评估。对下游用户而言，OpenFed 支持在不同学科背景下即插即用联邦学习，无需具备联邦学习的深厚专业知识。