# Limited Communications Distributed Optimization via Deep Unfolded Distributed ADMM arXiv: https://arxiv.org/abs/2309.14353 ยท HTML: https://mlsanity.com/paper/2309.14353/ ## Key facts - Cited by 2 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 69 references that are in the map. - First posted on arXiv 21 Sep 2023. ## Builds on (most cited first, 30 of 38) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017) - [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019) - [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021) - [Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing](https://mlsanity.com/paper/1912.10557/) (2019) - [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021) - [Model-Based Deep Learning](https://mlsanity.com/paper/2012.08405/) (2020) - [Graph Neural Networks for Scalable Radio Resource Management: Architecture Design and Theoretical Analysis](https://mlsanity.com/paper/2007.07632/) (2020) - [Learning to Detect](https://mlsanity.com/paper/1805.07631/) (2018) - [On the Linear Convergence of the ADMM in Decentralized Consensus Optimization](https://mlsanity.com/paper/1307.5561/) (2013) - [Model-Based Deep Learning: On the Intersection of Deep Learning and Optimization](https://mlsanity.com/paper/2205.02640/) (2022) - [Federated Learning: A Signal Processing Perspective](https://mlsanity.com/paper/2103.17150/) (2021) - [RouteNet: Leveraging Graph Neural Networks for network modeling and optimization in SDN](https://mlsanity.com/paper/1910.01508/) (2019) - [Communication-Efficient Algorithms for Decentralized and Stochastic Optimization](https://mlsanity.com/paper/1701.03961/) (2017) - [A general framework for decentralized optimization with first-order methods](https://mlsanity.com/paper/2009.05837/) (2020) - [DeepSIC: Deep Soft Interference Cancellation for Multiuser MIMO Detection](https://mlsanity.com/paper/2002.03214/) (2020) - [Multi-Agent Distributed Optimization via Inexact Consensus ADMM](https://mlsanity.com/paper/1402.6065/) (2014) - [Walk for Learning: A Random Walk Approach for Federated Learning from Heterogeneous Data](https://mlsanity.com/paper/2206.00737/) (2022) - [Convergence Rate of Distributed ADMM over Networks](https://mlsanity.com/paper/1601.00194/) (2016) - [Collaborative Inference for AI-Empowered IoT Devices](https://mlsanity.com/paper/2207.11664/) (2022) - [Deep Learning for Distributed Optimization: Applications to Wireless Resource Management](https://mlsanity.com/paper/1905.13378/) (2019) - [Discriminative and Generative Learning for Linear Estimation of Random Signals [Lecture Notes]](https://mlsanity.com/paper/2206.04432/) (2022) - [The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey](https://mlsanity.com/paper/2305.13854/) (2023) - [Distributed Linearized Alternating Direction Method of Multipliers for Composite Convex Consensus Optimization](https://mlsanity.com/paper/1512.08122/) (2015) - [Distributed Optimization Using the Primal-Dual Method of Multipliers](https://mlsanity.com/paper/1702.00841/) (2017) - [LoRD-Net: Unfolded Deep Detection Network with Low-Resolution Receivers](https://mlsanity.com/paper/2102.02993/) (2021) - [Learn to Rapidly and Robustly Optimize Hybrid Precoding](https://mlsanity.com/paper/2301.00369/) (2023) - [Decentralized Low-Latency Collaborative Inference via Ensembles on the Edge](https://mlsanity.com/paper/2206.03165/) (2022) - [Derivation and Analysis of the Primal-Dual Method of Multipliers Based on Monotone Operator Theory](https://mlsanity.com/paper/1706.02654/) (2017) - [Deep Task-Based Analog-to-Digital Conversion](https://mlsanity.com/paper/2201.12634/) (2022) ## Built on by (most cited first, 2 of 2) - [Robust Stochastically-Descending Unrolled Networks](https://mlsanity.com/paper/2312.15788/) (2023) - [Rapid and Power-Aware Learned Optimization for Modular Receive Beamforming](https://mlsanity.com/paper/2408.00439/) (2024) ## Abstract (arXiv) Distributed optimization is a fundamental framework for collaborative inference and decision making in decentralized multi-agent systems. The operation is modeled as the joint minimization of a shared objective which typically depends on observations gathered locally by each agent. Distributed optimization algorithms, such as the common D-ADMM, tackle this task by iteratively combining local computations and message exchanges. One of the main challenges associated with distributed optimization, and particularly with D-ADMM, is that it requires a large number of communications, i.e., messages exchanged between the agents, to reach consensus. This can make D-ADMM costly in power, latency, and channel resources. In this work we propose unfolded D-ADMM, which follows the emerging deep unfolding methodology to enable D-ADMM to operate reliably with a predefined and small number of messages exchanged by each agent. Unfolded D-ADMM fully preserves the operation of D-ADMM, while leveraging data to tune the hyperparameters of each iteration of the algorithm. These hyperparameters can either be agent-specific, aiming at achieving the best performance within a fixed number of iterations over a given network, or shared among the agents, allowing to learn to distributedly optimize over different networks. For both settings, our unfolded D-ADMM operates with limited communications, while preserving the interpretability and flexibility of the original D-ADMM algorithm. We specialize unfolded D-ADMM for two representative settings: a distributed estimation task, considering a sparse recovery setup, and a distributed learning scenario, where multiple agents collaborate in learning a machine learning model. Our numerical results demonstrate that the proposed approach dramatically reduces the number of communications utilized by D-ADMM, without compromising on its performance.