mlsanityData to 7 Oct 2026

Advances in APPFL: A Comprehensive and Extensible Federated Learning Framework

arXiv 2409.11585 ↗First posted 17 Sep 2024cs.LG cs.CR cs.DCcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 67 of its 106 references that are in the map. First posted on arXiv 17 Sep 2024.

Builds on

67 of 106 references.

References · 67

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38 more
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Citing papers per year: 2024: 1; 2025: 15; 2026: 3.

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