# Efficient Distributed DNNs in the Mobile-edge-cloud Continuum

arXiv: https://arxiv.org/abs/2202.11349 · HTML: https://mlsanity.com/paper/2202.11349/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 67 references that are in the map.
- First posted on arXiv 23 Feb 2022.

## Builds on (most cited first, 30 of 33)

- [Convex optimization](https://mlsanity.com/paper/2106.01946/) (2021)
- [On the Convergence of FedAvg on Non-IID Data](https://mlsanity.com/paper/1907.02189/) (2019)
- [Optimization Methods for Large-Scale Machine Learning](https://mlsanity.com/paper/1606.04838/) (2016)
- [Deep Clustering for Unsupervised Learning of Visual Features](https://mlsanity.com/paper/1807.05520/) (2018)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [Adaptive Federated Learning in Resource Constrained Edge Computing Systems](https://mlsanity.com/paper/1804.05271/) (2018)
- [Split learning for health: Distributed deep learning without sharing raw patient data](https://mlsanity.com/paper/1812.00564/) (2018)
- [Fair Resource Allocation in Federated Learning](https://mlsanity.com/paper/1905.10497/) (2019)
- [Distributed learning of deep neural network over multiple agents](https://mlsanity.com/paper/1810.06060/) (2018)
- [Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learning](https://mlsanity.com/paper/2002.05651/) (2020)
- [Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing](https://mlsanity.com/paper/1905.10083/) (2019)
- [Local Aggregation for Unsupervised Learning of Visual Embeddings](https://mlsanity.com/paper/1903.12355/) (2019)
- [Federated Optimization:Distributed Optimization Beyond the Datacenter](https://mlsanity.com/paper/1511.03575/) (2015)
- [Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing](https://mlsanity.com/paper/1910.05316/) (2019)
- [Communication Efficient Distributed Optimization using an Approximate Newton-type Method](https://mlsanity.com/paper/1312.7853/) (2013)
- [Blockchained On-Device Federated Learning](https://mlsanity.com/paper/1808.03949/) (2018)
- [Reliable Federated Learning for Mobile Networks](https://mlsanity.com/paper/1910.06837/) (2019)
- [In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning](https://mlsanity.com/paper/1809.07857/) (2018)
- [Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligence](https://mlsanity.com/paper/1909.00560/) (2019)
- [End-to-End Evaluation of Federated Learning and Split Learning for Internet of Things](https://mlsanity.com/paper/2003.13376/) (2020)
- [Fast-Convergent Federated Learning](https://mlsanity.com/paper/2007.13137/) (2020)
- [srsLTE: An Open-Source Platform for LTE Evolution and Experimentation](https://mlsanity.com/paper/1602.04629/) (2016)
- [Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1906.04585/) (2019)
- [Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition](https://mlsanity.com/paper/2004.10016/) (2020)
- [6G White Paper on Edge Intelligence](https://mlsanity.com/paper/2004.14850/) (2020)
- [DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptation](https://mlsanity.com/paper/2103.12768/) (2021)
- [Inferring Latent Domains for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/2103.13873/) (2021)
- [Distributed Deep Q-Learning](https://mlsanity.com/paper/1508.04186/) (2015)
- [Active Learning-based Classification in Automated Connected Vehicles](https://mlsanity.com/paper/2002.07593/) (2020)

## Built on by (most cited first, 1 of 1)

- [Dependable Distributed Training of Compressed Machine Learning Models](https://mlsanity.com/paper/2402.14346/) (2024)

## Abstract (arXiv)

In the mobile-edge-cloud continuum, a plethora of heterogeneous data sources and computation-capable nodes are available. Such nodes can cooperate to perform a distributed learning task, aided by a learning controller (often located at the network edge). The controller is required to make decisions concerning (i) data selection, i.e., which data sources to use; (ii) model selection, i.e., which machine learning model to adopt, and (iii) matching between the layers of the model and the available physical nodes. All these decisions influence each other, to a significant extent and often in counter-intuitive ways. In this paper, we formulate a problem addressing all of the above aspects and present a solution concept called RightTrain, aiming at making the aforementioned decisions in a joint manner, minimizing energy consumption subject to learning quality and latency constraints. RightTrain leverages an expanded-graph representation of the system and a delay-aware Steiner tree to obtain a provably near-optimal solution while keeping the time complexity low. Specifically, it runs in polynomial time and its decisions exhibit a competitive ratio of 2(1+ε), outperforming state-of-the-art solutions by over 50%. Our approach is also validated through a real-world implementation.