# Networked Signal and Information Processing

arXiv: https://arxiv.org/abs/2210.13767 · HTML: https://mlsanity.com/paper/2210.13767/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 92 references that are in the map.
- First posted on arXiv 25 Oct 2022.

## Builds on (most cited first, 30 of 32)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent](https://mlsanity.com/paper/1705.09056/) (2017)
- [Graph Signal Processing: Overview, Challenges and Applications](https://mlsanity.com/paper/1712.00468/) (2017)
- [Escaping From Saddle Points --- Online Stochastic Gradient for Tensor Decomposition](https://mlsanity.com/paper/1503.02101/) (2015)
- [EXTRA: An Exact First-Order Algorithm for Decentralized Consensus Optimization](https://mlsanity.com/paper/1404.6264/) (2014)
- [Achieving Geometric Convergence for Distributed Optimization over Time-Varying Graphs](https://mlsanity.com/paper/1607.03218/) (2016)
- [D2: Decentralized Training over Decentralized Data](https://mlsanity.com/paper/1803.07068/) (2018)
- [NEXT: In-Network Nonconvex Optimization](https://mlsanity.com/paper/1602.00591/) (2016)
- [Distributed optimization over time-varying directed graphs](https://mlsanity.com/paper/1303.2289/) (2013)
- [On the Linear Convergence of the ADMM in Decentralized Consensus Optimization](https://mlsanity.com/paper/1307.5561/) (2013)
- [A decentralized proximal-gradient method with network independent step-sizes and separated convergence rates](https://mlsanity.com/paper/1704.07807/) (2017)
- [On the Influence of Bias-Correction on Distributed Stochastic Optimization](https://mlsanity.com/paper/1903.10956/) (2019)
- [Multitask learning over graphs: An Approach for Distributed, Streaming Machine Learning](https://mlsanity.com/paper/2001.02112/) (2020)
- [On the Learning Behavior of Adaptive Networks - Part I: Transient Analysis](https://mlsanity.com/paper/1312.7581/) (2013)
- [Decentralized Proximal Gradient Algorithms with Linear Convergence Rates](https://mlsanity.com/paper/1909.06479/) (2019)
- [DSA: Decentralized Double Stochastic Averaging Gradient Algorithm](https://mlsanity.com/paper/1506.04216/) (2015)
- [Exact Diffusion for Distributed Optimization and Learning --- Part II: Convergence Analysis](https://mlsanity.com/paper/1702.05142/) (2017)
- [Distributed Learning in Non-Convex Environments -- Part II: Polynomial Escape from Saddle-Points](https://mlsanity.com/paper/1907.01849/) (2019)
- [Learning over Multitask Graphs -- Part I: Stability Analysis](https://mlsanity.com/paper/1805.08535/) (2018)
- [Decentralized Stochastic Optimization with Inherent Privacy Protection](https://mlsanity.com/paper/2205.03884/) (2022)
- [Tutorial on dynamic average consensus: the problem, its applications, and the algorithms](https://mlsanity.com/paper/1803.04628/) (2018)
- [Asynchronous Adaptation and Learning over Networks --- Part I: Modeling and Stability Analysis](https://mlsanity.com/paper/1312.5434/) (2013)
- [Primal-dual methods for large-scale and distributed convex optimization and data analytics](https://mlsanity.com/paper/1912.08546/) (2019)
- [Graph-Homomorphic Perturbations for Private Decentralized Learning](https://mlsanity.com/paper/2010.12288/) (2020)
- [Excess-Risk of Distributed Stochastic Learners](https://mlsanity.com/paper/1302.1157/) (2013)
- [Stability and Performance Limits of Adaptive Primal-Dual Networks](https://mlsanity.com/paper/1408.3693/) (2014)
- [Distributed Subgradient Projection Algorithm over Directed Graphs: Alternate Proof](https://mlsanity.com/paper/1706.07707/) (2017)
- [Distributed Gradient Flow: Nonsmoothness, Nonconvexity, and Saddle Point Evasion](https://mlsanity.com/paper/2008.05387/) (2020)
- [Distributed Constrained Recursive Nonlinear Least-Squares Estimation: Algorithms and Asymptotics](https://mlsanity.com/paper/1602.00382/) (2016)
- [Adaptive Penalty-Based Distributed Stochastic Convex Optimization](https://mlsanity.com/paper/1312.4415/) (2013)

## Built on by (most cited first, 10 of 10)

- [Peer-to-Peer Learning Dynamics of Wide Neural Networks](https://mlsanity.com/paper/2409.15267/) (2024)
- [Online Graph Filtering Over Expanding Graphs](https://mlsanity.com/paper/2409.07204/) (2024)
- [High-Probability Convergence Guarantees of Decentralized SGD](https://mlsanity.com/paper/2510.06141/) (2025)
- [A Unified Framework for Center-based Clustering of Distributed Data](https://mlsanity.com/paper/2402.01302/) (2024)
- [Deep-Relative-Trust-Based Diffusion for Decentralized Deep Learning](https://mlsanity.com/paper/2501.03162/) (2025)
- [Scalable Min-Max Optimization via Primal-Dual Exact Pareto Optimization](https://mlsanity.com/paper/2504.02833/) (2025)
- [Quantitative Error Feedback for Quantization Noise Reduction of Filtering over Graphs](https://mlsanity.com/paper/2506.01404/) (2025)
- [On the Escaping Efficiency of Distributed Adversarial Training Algorithms](https://mlsanity.com/paper/2509.11337/) (2025)
- [Distributed Gradient Clustering: Convergence and the Effect of Initialization](https://mlsanity.com/paper/2603.20507/) (2026)
- [Decentralized Multitask Learning over Learned Task Graphs](https://mlsanity.com/paper/2608.26989/) (2026)

## Abstract (arXiv)

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.