Adaptive Consensus ADMM for Distributed Optimization
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Abstract (arXiv)
The alternating direction method of multipliers (ADMM) is commonly used for distributed model fitting problems, but its performance and reliability depend strongly on user-defined penalty parameters. We study distributed ADMM methods that boost performance by using different fine-tuned algorithm parameters on each worker node. We present a O(1/k) convergence rate for adaptive ADMM methods with node-specific parameters, and propose adaptive consensus ADMM (ACADMM), which automatically tunes parameters without user oversight.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Jun 2017.
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Citing papers per year: 2022: 3; 2023: 0; 2024: 1; 2025: 1.
5 papers.
- 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learningcited by 20
- 2022Survey of Matrix Completion Algorithmscited by 2
- 2025DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimizationcited by 1
- 2022Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent Systemcited by 0
- 2024Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignmentcited by 0
- 2025DiffCoord: Differentiable Coordination for Distributed Multi-Agent Trajectory Optimization1 Sep 2025
- 2024Compressed Sensor Caching and Collaborative Sparse Data Recovery with Anchor Alignment14 Jun 2024
- 2022Secure Distributed/Federated Learning: Prediction-Privacy Trade-Off for Multi-Agent System24 Apr 2022
- 2022Survey of Matrix Completion Algorithms1 Apr 2022
- 2022APPFL: Open-Source Software Framework for Privacy-Preserving Federated Learning8 Feb 2022