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 open-sourced at https://github.com/APPFL/APPFL.

本研究解决了现有联邦学习（FL）框架在异构性和安全性方面的不足，提出了一个名为APPFL的可扩展框架及基准套件。APPFL不仅提供了全面的解决方案，还具备灵活性，能够轻松整合新算法或适应新应用。通过多项实验，证明了APPFL在通信效率、隐私保护、计算性能和资源利用率等方面的出色能力。

APPFL的进展：一个综合且可扩展的联邦学习框架