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Communication-Efficient Distributed Asynchronous ADMM

arXiv 2508.12233 ↗First posted 17 Aug 2025cs.LG eess.SPcited by 0

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Abstract (arXiv)

In distributed optimization and federated learning, asynchronous alternating direction method of multipliers (ADMM) serves as an attractive option for large-scale optimization, data privacy, straggler nodes and variety of objective functions. However, communication costs can become a major bottleneck when the nodes have limited communication budgets or when the data to be communicated is prohibitively large. In this work, we propose introducing coarse quantization to the data to be exchanged in aynchronous ADMM so as to reduce communication overhead for large-scale federated learning and distributed optimization applications. We experimentally verify the convergence of the proposed method for several distributed learning tasks, including neural networks.

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References · 10

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,538
  3. 2018signSGD: Compressed Optimisation for Non-Convex Problemscited by 571
  4. 2018Sparsified SGD with Memorycited by 376
  5. 2019Error Feedback Fixes SignSGD and other Gradient Compression Schemescited by 310
  6. 2019Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification, and Local Computationscited by 181
  7. 2020Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learningcited by 22
  8. 2021Communication-Efficient ADMM-based Federated Learningcited by 14
  9. 2021Differentially Private Federated Learning via Inexact ADMMcited by 11
  10. 2015Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysiscited by 10

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  3. 2023Asynchronous Federated Learning with Bidirectional Quantized Communications and Buffered Aggregation
  4. 2023Efficient Asynchronous Federated Learning with Sparsification and Quantization
  5. 2024Truncated Non-Uniform Quantization for Distributed SGD
  6. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approach
  7. 20211-Bit Compressive Sensing for Efficient Federated Learning Over the Air
  8. 2024Quantization Avoids Saddle Points in Distributed Optimization