mlsanityData to 7 Oct 2026

Adaptive Scheduling for Machine Learning Tasks over Networks

arXiv 2101.10007 ↗First posted 25 Jan 2021cs.LG cs.SY eess.SYcited by 2

Not on the map. Why?

Abstract (arXiv)

A key functionality of emerging connected autonomous systems such as smart transportation systems, smart cities, and the industrial Internet-of-Things, is the ability to process and learn from data collected at different physical locations. This is increasingly attracting attention under the terms of distributed learning and federated learning. However, in this setup data transfer takes place over communication resources that are shared among many users and tasks or subject to capacity constraints. This paper examines algorithms for efficiently allocating resources to linear regression tasks by exploiting the informativeness of the data. The algorithms developed enable adaptive scheduling of learning tasks with reliable performance guarantees.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 28 references that are in the map. First posted on arXiv 25 Jan 2021.

Builds on

21 of 28 references.

References · 21

  1. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  2. 2019Towards Federated Learning at Scale: System Designcited by 826
  3. 2017Federated Multi-Task Learningcited by 669
  4. 2019FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantizationcited by 349
  5. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  6. 2017Sparse Communication for Distributed Gradient Descentcited by 243
  7. 2019Federated Learning over Wireless Fading Channelscited by 178
  8. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  9. 2018LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learningcited by 95
  10. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  11. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  12. 2019On Analog Gradient Descent Learning over Multiple Access Fading Channelscited by 61
9 more
  1. 2018Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communicationcited by 51
  2. 2019Accelerating DNN Training in Wireless Federated Edge Learning Systemscited by 34
  3. 2019Machine Learning in the Aircited by 32
  4. 2020Cooperative Learning via Federated Distillation over Fading Channelscited by 16
  5. 2019Age-of-Information vs. Value-of-Information Scheduling for Cellular Networked Control Systemscited by 11
  6. 2020Communication Efficient Federated Learning over Multiple Access Channelscited by 11
  7. 2018Control Aware Radio Resource Allocation in Low Latency Wireless Control Systemscited by 7
  8. 2018Event-triggered Learning for Resource-efficient Networked Controlcited by 4
  9. 2020Event-Based Control for Online Training of Neural Networkscited by 3

Built on by

Cited by year

Citing papers per year: 2021: 2.

2 papers.

  1. 2021Linear Regression over Networks with Communication Guaranteescited by 2
  2. 2021Federated Reinforcement Learning at the Edgecited by 1