mlsanityData to 7 Oct 2026

RHFedMTL: Resource-Aware Hierarchical Federated Multi-Task Learning

arXiv 2306.00675 ↗First posted 1 Jun 2023cs.NI cs.LGcited by 4

Not on the map. Why?

Abstract (arXiv)

The rapid development of artificial intelligence (AI) over massive applications including Internet-of-things on cellular network raises the concern of technical challenges such as privacy, heterogeneity and resource efficiency. Federated learning is an effective way to enable AI over massive distributed nodes with security. However, conventional works mostly focus on learning a single global model for a unique task across the network, and are generally less competent to handle multi-task learning (MTL) scenarios with stragglers at the expense of acceptable computation and communication cost. Meanwhile, it is challenging to ensure the privacy while maintain a coupled multi-task learning across multiple base stations (BSs) and terminals. In this paper, inspired by the natural cloud-BS-terminal hierarchy of cellular works, we provide a viable resource-aware hierarchical federated MTL (RHFedMTL) solution to meet the heterogeneity of tasks, by solving different tasks within the BSs and aggregating the multi-task result in the cloud without compromising the privacy. Specifically, a primal-dual method has been leveraged to effectively transform the coupled MTL into some local optimization sub-problems within BSs. Furthermore, compared with existing methods to reduce resource cost by simply changing the aggregation frequency, we dive into the intricate relationship between resource consumption and learning accuracy, and develop a resource-aware learning strategy for local terminals and BSs to meet the resource budget. Extensive simulation results demonstrate the effectiveness and superiority of RHFedMTL in terms of improving the learning accuracy and boosting the convergence rate.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 25 of its 50 references that are in the map. First posted on arXiv 1 Jun 2023.

Builds on

25 of 50 references.

References · 25

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2021Convex optimizationcited by 2,479
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,461
  5. 2022Planning-oriented Autonomous Drivingcited by 1,068
  6. 2017A Survey on Multi-Task Learningcited by 838
  7. 2017An Overview of Multi-Task Learning in Deep Neural Networkscited by 791
  8. 2017Federated Multi-Task Learningcited by 669
  9. 2018Federated Learning for Mobile Keyboard Predictioncited by 565
  10. 2016Federated Optimization: Distributed Machine Learning for On-Device Intelligencecited by 558
  11. 2019Federated Learning with Differential Privacy: Algorithms and Performance Analysiscited by 528
  12. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
13 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  2. 2019Client-Edge-Cloud Hierarchical Federated Learningcited by 202
  3. 2021Federated Multi-Task Learning under a Mixture of Distributionscited by 189
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2018Expanding the Reach of Federated Learning by Reducing Client Resource Requirementscited by 162
  6. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  7. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  8. 2020Convergence Time Optimization for Federated Learning over Wireless Networkscited by 81
  9. 2016CoCoA: A General Framework for Communication-Efficient Distributed Optimizationcited by 69
  10. 2020Privacy For Free: Wireless Federated Learning Via Uncoded Transmission With Adaptive Power Controlcited by 62
  11. 2020Wireless Federated Learning with Local Differential Privacycited by 50
  12. 2023Recent Advances on Federated Learning: A Systematic Surveycited by 40
  13. 2014Communication-Efficient Distributed Dual Coordinate Ascentcited by 30

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 2.

4 papers.

  1. 2024Data Similarity-Based One-Shot Clustering for Multi-Task Hierarchical Federated Learningcited by 3
  2. 2025Tool-Aided Evolutionary LLM for Generative Policy Toward Efficient Resource Management in Wireless Federated Learningcited by 2
  3. 2026Graph-Structured Deep Learning Framework for Multi-task Contention Identification with High-dimensional Metricscited by 1
  4. 2026Hierarchical Multi-Task Federated Learning in VANETscited by 0