mlsanityData to 7 Oct 2026

A Primal-Dual Algorithm for Hybrid Federated Learning

arXiv 2210.08106 ↗First posted 14 Oct 2022cs.LGcited by 4

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Abstract (arXiv)

Very few methods for hybrid federated learning, where clients only hold subsets of both features and samples, exist. Yet, this scenario is extremely important in practical settings. We provide a fast, robust algorithm for hybrid federated learning that hinges on Fenchel Duality. We prove the convergence of the algorithm to the same solution as if the model is trained centrally in a variety of practical regimes. Furthermore, we provide experimental results that demonstrate the performance improvements of the algorithm over a commonly used method in federated learning, FedAvg, and an existing hybrid FL algorithm, HyFEM. We also provide privacy considerations and necessary steps to protect client data.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 9 of its 33 references that are in the map. First posted on arXiv 14 Oct 2022.

Builds on

9 of 33 references.

References · 9

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  3. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  4. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  5. 2014Communication-Efficient Distributed Dual Coordinate Ascentcited by 30
  6. 2020Hybrid Federated Learning: Algorithms and Implementationcited by 14
  7. 2016Optimization for Large-Scale Machine Learning with Distributed Features and Observationscited by 3
  8. 2022Comprehensive Performance Analysis of Homomorphic Cryptosystems for Practical Data Processingcited by 3
  9. 2022A dual approach for federated learningcited by 2

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Cited by year

Citing papers per year: 2024: 3; 2025: 1.

4 papers.

  1. 2024Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural networkcited by 1
  2. 2024Federated Automated Feature Engineeringcited by 1
  3. 2024A Hybrid Federated Kernel Regularized Least Squares Algorithmcited by 0
  4. 2025Unveiling Hidden Threats: Using Fractal Triggers to Boost Stealthiness of Distributed Backdoor Attacks in Federated Learningcited by 0