打造可扩展的联邦学习框架，解决数据异构与安全难题
Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework
- 设计可扩展框架，支持多种联邦学习场景
- 实测通信效率、隐私保护与资源利用表现优异
- 适合研究者快速集成新算法或部署到医疗电网等场景
联邦学习（FL）是一种分布式机器学习范式，可在保护数据隐私的前提下实现协同模型训练。在当前多数数据具有专属性、机密性且分布于各地的背景下，FL成为有效利用此类数据的有前景方法，尤其适用于医疗和电网等敏感领域。然而，异构性和安全性仍是主要挑战，现有框架或未能充分应对，或缺乏灵活性以集成新方案。为此，我们介绍了APPFL的最新进展——一个可扩展的联邦学习框架与基准测试套件，提供对异构性与安全性的全面解决方案，并具备友好的接口以集成新算法或适配新应用。通过大量实验评估了通信效率、隐私保护、计算性能与资源利用率等多方面表现。进一步通过垂直、分层与去中心化联邦学习的案例研究展示了其可扩展性。APPFL已在GitHub开源：https://github.com/APPFL/APPFL。
原文摘要 · Abstract (English)
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a promising approach to leverage such data effectively, particularly in sensitive domains such as medicine and the electric grid. Heterogeneity and security are the key challenges in FL, however, most existing FL frameworks either fail to address these challenges adequately or lack the flexibility to incorporate new solutions. To this end, we present the recent advances in developing APPFL, an extensible framework and benchmarking suite for federated learning, which offers comprehensive solutions for heterogeneity and security concerns, as well as user-friendly interfaces for integrating new algorithms or adapting to new applications. We demonstrate the capabilities of APPFL through extensive experiments evaluating various aspects of FL, including communication efficiency, privacy preservation, computational performance, and resource utilization. We further highlight the extensibility of APPFL through case studies in vertical, hierarchical, and decentralized FL. APPFL is fully open-sourced on GitHub at https://github.com/APPFL/APPFL.
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