# Robust, Deep, and Reinforcement Learning for Management of Communication and Power Networks

arXiv: https://arxiv.org/abs/2202.05395 · HTML: https://mlsanity.com/paper/2202.05395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 37 of its 235 references that are in the map.
- First posted on arXiv 8 Feb 2022.

## Builds on (most cited first, 30 of 37)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [Certifying and removing disparate impact](https://mlsanity.com/paper/1412.3756/) (2014)
- [Ensemble Distillation for Robust Model Fusion in Federated Learning](https://mlsanity.com/paper/2006.07242/) (2020)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [On Gradient Descent Ascent for Nonconvex-Concave Minimax Problems](https://mlsanity.com/paper/1906.00331/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Adversarial Attacks and Defences: A Survey](https://mlsanity.com/paper/1810.00069/) (2018)
- [Towards Deep Neural Network Architectures Robust to Adversarial Examples](https://mlsanity.com/paper/1412.5068/) (2014)
- [UVeQFed: Universal Vector Quantization for Federated Learning](https://mlsanity.com/paper/2006.03262/) (2020)
- [Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies](https://mlsanity.com/paper/2003.00653/) (2020)
- [Physics-Guided Deep Neural Networks for Power Flow Analysis](https://mlsanity.com/paper/2002.00097/) (2020)
- [Data-driven Optimal Cost Selection for Distributionally Robust Optimization](https://mlsanity.com/paper/1705.07152/) (2017)
- [Physics-Aware Neural Networks for Distribution System State Estimation](https://mlsanity.com/paper/1903.09669/) (2019)
- [Fairness Constraints: Mechanisms for Fair Classification](https://mlsanity.com/paper/1507.05259/) (2015)
- [Combating Adversarial Attacks Using Sparse Representations](https://mlsanity.com/paper/1803.03880/) (2018)
- [Optimal Tap Setting of Voltage Regulation Transformers Using Batch Reinforcement Learning](https://mlsanity.com/paper/1807.10997/) (2018)
- [Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing](https://mlsanity.com/paper/1812.08593/) (2018)
- [Minimum Uncertainty Based Detection of Adversaries in Deep Neural Networks](https://mlsanity.com/paper/1904.02841/) (2019)
- [Autonomous Voltage Control for Grid Operation Using Deep Reinforcement Learning](https://mlsanity.com/paper/1904.10597/) (2019)
- [Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control](https://mlsanity.com/paper/1604.08382/) (2016)
- [Physics-Informed Deep Neural Network Method for Limited Observability State Estimation](https://mlsanity.com/paper/1910.06401/) (2019)
- [A Transfer Learning Approach for Cache-Enabled Wireless Networks](https://mlsanity.com/paper/1503.05448/) (2015)
- [User-Level Privacy-Preserving Federated Learning: Analysis and Performance Optimization](https://mlsanity.com/paper/2003.00229/) (2020)
- [Learning Non-Discriminatory Predictors](https://mlsanity.com/paper/1702.06081/) (2017)
- [Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data](https://mlsanity.com/paper/2007.03724/) (2020)
- [A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach](https://mlsanity.com/paper/2011.06021/) (2020)

## Abstract (arXiv)

This thesis develops data-driven machine learning algorithms to managing and optimizing the next-generation highly complex cyberphysical systems, which desperately need ground-breaking control, monitoring, and decision making schemes that can guarantee robustness, scalability, and situational awareness. The present thesis first develops principled methods to make generic machine learning models robust against distributional uncertainties and adversarial data. Particular focus will be on parametric models where some training data are being used to learn a parametric model. The developed framework is of high interest especially when training and testing data are drawn from "slightly" different distribution. We then introduce distributionally robust learning frameworks to minimize the worst-case expected loss over a prescribed ambiguity set of training distributions quantified via Wasserstein distance. Later, we build on this robust framework to design robust semi-supervised learning over graph methods. The second part of this thesis aspires to fully unleash the potential of next-generation wired and wireless networks, where we design "smart" network entities using (deep) reinforcement learning approaches. Finally, this thesis enhances the power system operation and control. Our contribution is on sustainable distribution grids with high penetration of renewable sources and demand response programs. To account for unanticipated and rapidly changing renewable generation and load consumption scenarios, we specifically delegate reactive power compensation to both utility-owned control devices (e.g., capacitor banks), as well as smart inverters of distributed generation units with cyber-capabilities.