mlsanityData to 7 Oct 2026

APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning

arXiv 2202.03672 ↗First posted 8 Feb 2022cs.LGcited by 20

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Abstract (arXiv)

Federated learning (FL) enables training models at different sites and updating the weights from the training instead of transferring data to a central location and training as in classical machine learning. The FL capability is especially important to domains such as biomedicine and smart grid, where data may not be shared freely or stored at a central location because of policy challenges. Thanks to the capability of learning from decentralized datasets, FL is now a rapidly growing research field, and numerous FL frameworks have been developed. In this work, we introduce APPFL, the Argonne Privacy-Preserving Federated Learning framework. APPFL allows users to leverage implemented privacy-preserving algorithms, implement new algorithms, and simulate and deploy various FL algorithms with privacy-preserving techniques. The modular framework enables users to customize the components for algorithms, privacy, communication protocols, neural network models, and user data. We also present a new communication-efficient algorithm based on an inexact alternating direction method of multipliers. The algorithm requires significantly less communication between the server and the clients than does the current state of the art. We demonstrate the computational capabilities of APPFL, including differentially private FL on various test datasets and its scalability, by using multiple algorithms and datasets on different computing environments.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 13 of its 27 references that are in the map. First posted on arXiv 8 Feb 2022.

Builds on

13 of 27 references.

References · 13

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
  3. 2018LEAF: A Benchmark for Federated Settingscited by 780
  4. 2020Inverting Gradients -- How easy is it to break privacy in federated learning?cited by 707
  5. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  6. 2020FedML: A Research Library and Benchmark for Federated Machine Learningcited by 298
  7. 2019Can You Really Backdoor Federated Learning?cited by 250
  8. 2020A Framework for Evaluating Gradient Leakage Attacks in Federated Learningcited by 63
  9. 2019Differential Privacy-enabled Federated Learning for Sensitive Health Datacited by 44
  10. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  11. 2021Differentially Private Federated Learning via Inexact ADMMcited by 11
  12. 2017Adaptive Consensus ADMM for Distributed Optimizationcited by 5
1 more
  1. 2021A Privacy-Preserving Distributed Control of Optimal Power Flowcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 6; 2024: 8; 2025: 2; 2026: 4.

20 papers.

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  1. 2024Foundation Models for the Electric Power Gridcited by 27
  2. 2024Advances in APPFL: A Comprehensive and Extensible Federated Learning Frameworkcited by 19
  3. 2023FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices using a Computing Power Aware Schedulercited by 13
  4. 2024Privacy Preserving Federated Learning in Medical Imaging with Uncertainty Estimationcited by 9
  5. 2023APPFLx: Providing Privacy-Preserving Cross-Silo Federated Learning as a Servicecited by 4
  6. 2024FLEX: FLEXible Federated Learning Frameworkcited by 4
  7. 2023Federated Short-Term Load Forecasting with Personalization Layers for Heterogeneous Clientscited by 3
  8. 2024Secure Federated Learning Across Heterogeneous Cloud and High-Performance Computing Resources -- A Case Study on Federated Fine-tuning of LLaMA 2cited by 3
  9. 2024Future-Proofing Medical Imaging with Privacy-Preserving Federated Learning and Uncertainty Quantification: A Reviewcited by 2
  10. 2023Privacy-Preserving Load Forecasting via Personalized Model Obfuscationcited by 1
  11. 2024Addressing Heterogeneity in Federated Load Forecasting with Personalization Layerscited by 1
  12. 2026Scalable Cross-Facility Federated Learning for Scientific Foundation Models on Multiple Supercomputerscited by 1
8 more
  1. 2023Differentially Private Distributed Convex Optimizationcited by 0
  2. 2023A Comprehensive Empirical Study of Bugs in Open-Source Federated Learning Frameworkscited by 0
  3. 2024Federated Computing -- Survey on Building Blocks, Extensions and Systemscited by 0
  4. 2025CADRE: Customizable Assurance of Data Readiness in Privacy-Preserving Federated Learningcited by 0
  5. 2025Differentially private federated learning for localized control of infectious disease dynamicscited by 0
  6. 2026FedQueue: Queue-Aware Federated Learning for Cross-Facility HPC Trainingcited by 0
  7. 2026Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deploymentcited by 0
  8. 2026Making Cross-Continental Federated Learning Repeatable with FLIP: a Multi-Application Studycited by 0