# Dynamic Network-Assisted D2D-Aided Coded Distributed Learning

arXiv: https://arxiv.org/abs/2111.14789 · HTML: https://mlsanity.com/paper/2111.14789/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 48 references that are in the map.
- First posted on arXiv 26 Nov 2021.

## Builds on (most cited first, 19 of 19)

- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Federated Learning: Strategies for Improving Communication Efficiency](https://mlsanity.com/paper/1610.05492/) (2016)
- [Federated Learning in Mobile Edge Networks: A Comprehensive Survey](https://mlsanity.com/paper/1909.11875/) (2019)
- [A Joint Learning and Communications Framework for Federated Learning over Wireless Networks](https://mlsanity.com/paper/1909.07972/) (2019)
- [Federated Learning via Over-the-Air Computation](https://mlsanity.com/paper/1812.11750/) (2018)
- [Broadband Analog Aggregation for Low-Latency Federated Edge Learning (Extended Version)](https://mlsanity.com/paper/1812.11494/) (2018)
- [Energy Efficient Federated Learning Over Wireless Communication Networks](https://mlsanity.com/paper/1911.02417/) (2019)
- [Convergence Time Optimization for Federated Learning over Wireless Networks](https://mlsanity.com/paper/2001.07845/) (2020)
- [Scheduling for Cellular Federated Edge Learning with Importance and Channel Awareness](https://mlsanity.com/paper/2004.00490/) (2020)
- [Training (Overparametrized) Neural Networks in Near-Linear Time](https://mlsanity.com/paper/2006.11648/) (2020)
- [Differential Privacy as a Mutual Information Constraint](https://mlsanity.com/paper/1608.03677/) (2016)
- [Coded Computing for Low-Latency Federated Learning over Wireless Edge Networks](https://mlsanity.com/paper/2011.06223/) (2020)
- [Differential Privacy Techniques for Cyber Physical Systems: A Survey](https://mlsanity.com/paper/1812.02282/) (2018)
- [Coded Federated Learning](https://mlsanity.com/paper/2002.09574/) (2020)
- [Privacy-Utility Trade-off of Linear Regression under Random Projections and Additive Noise](https://mlsanity.com/paper/1902.04688/) (2019)
- [The Disruptions of 5G on Data-driven Technologies and Applications](https://mlsanity.com/paper/1909.08096/) (2019)
- [Distributed Sketching Methods for Privacy Preserving Regression](https://mlsanity.com/paper/2002.06538/) (2020)
- [Comparison Between Genetic Fuzzy Methodology and Q-learning for Collaborative Control Design](https://mlsanity.com/paper/2008.12678/) (2020)

## Built on by (most cited first, 2 of 2)

- [Matching DNN Compression and Cooperative Training with Resources and Data Availability](https://mlsanity.com/paper/2212.02304/) (2022)
- [Distributed Learning based on 1-Bit Gradient Coding in the Presence of Stragglers](https://mlsanity.com/paper/2403.14716/) (2024)

## Abstract (arXiv)

Today, various machine learning (ML) applications offer continuous data processing and real-time data analytics at the edge of a wireless network. Distributed real-time ML solutions are highly sensitive to the so-called straggler effect caused by resource heterogeneity and alleviated by various computation offloading mechanisms that seriously challenge the communication efficiency, especially in large-scale scenarios. To decrease the communication overhead, we rely on device-to-device (D2D) connectivity that improves spectrum utilization and allows efficient data exchange between devices in proximity. In particular, we design a novel D2D-aided coded federated learning method (D2D-CFL) for efficient load balancing across devices. The proposed solution captures system dynamics, including data (time-dependent learning model, varied intensity of data arrivals), device (diverse computational resources and volume of training data), and deployment (varied locations and D2D graph connectivity). To minimize the number of communication rounds, we derive an optimal compression rate for achieving minimum processing time and establish its connection with the convergence time. The resulting optimization problem provides suboptimal compression parameters, which improve the total training time. Our proposed method is beneficial for real-time collaborative applications, where the users continuously generate training data resulting in the model drift.