mlsanityData to 7 Oct 2026

Efficient Distributed DNNs in the Mobile-edge-cloud Continuum

arXiv 2202.11349 ↗First posted 23 Feb 2022cs.NI cs.LGcited by 1

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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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 33 of its 67 references that are in the map. First posted on arXiv 23 Feb 2022.

Builds on

33 of 67 references.

References · 33

  1. 2021Convex optimizationcited by 2,479
  2. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  3. 2016Optimization Methods for Large-Scale Machine Learningcited by 1,140
  4. 2018Deep Clustering for Unsupervised Learning of Visual Featurescited by 1,020
  5. 2019Green AIcited by 620
  6. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
  7. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  8. 2018Split learning for health: Distributed deep learning without sharing raw patient datacited by 391
  9. 2019Fair Resource Allocation in Federated Learningcited by 389
  10. 2018Distributed learning of deep neural network over multiple agentscited by 277
  11. 2020Towards the Systematic Reporting of the Energy and Carbon Footprints of Machine Learningcited by 246
  12. 2019Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computingcited by 225
21 more
  1. 2019Local Aggregation for Unsupervised Learning of Visual Embeddingscited by 204
  2. 2015Federated Optimization:Distributed Optimization Beyond the Datacentercited by 193
  3. 2019Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computingcited by 150
  4. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  5. 2018Blockchained On-Device Federated Learningcited by 105
  6. 2019Reliable Federated Learning for Mobile Networkscited by 92
  7. 2018In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learningcited by 80
  8. 2019Edge Intelligence: The Confluence of Edge Computing and Artificial Intelligencecited by 75
  9. 2020End-to-End Evaluation of Federated Learning and Split Learning for Internet of Thingscited by 64
  10. 2020Fast-Convergent Federated Learningcited by 61
  11. 2016srsLTE: An Open-Source Platform for LTE Evolution and Experimentationcited by 16
  12. 2019Gossip-based Actor-Learner Architectures for Deep Reinforcement Learningcited by 16
  13. 2020Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognitioncited by 15
  14. 20206G White Paper on Edge Intelligencecited by 14
  15. 2021DA4Event: towards bridging the Sim-to-Real Gap for Event Cameras using Domain Adaptationcited by 14
  16. 2021Inferring Latent Domains for Unsupervised Deep Domain Adaptationcited by 11
  17. 2015Distributed Deep Q-Learningcited by 8
  18. 2020Active Learning-based Classification in Automated Connected Vehiclescited by 7
  19. 2020DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systemscited by 7
  20. 2021Towards Inference Delivery Networks: Distributing Machine Learning with Optimality Guaranteescited by 3
  21. 2019OKpi: All-KPI Network Slicing Through Efficient Resource Allocationcited by 2

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