mlsanityData to 7 Oct 2026

Time-triggered Federated Learning over Wireless Networks

arXiv 2204.12426 ↗First posted 26 Apr 2022cs.LG cs.SY eess.SYcited by 10

Not on the map. Why?

Abstract (arXiv)

The newly emerging federated learning (FL) framework offers a new way to train machine learning models in a privacy-preserving manner. However, traditional FL algorithms are based on an event-triggered aggregation, which suffers from stragglers and communication overhead issues. To address these issues, in this paper, we present a time-triggered FL algorithm (TT-Fed) over wireless networks, which is a generalized form of classic synchronous and asynchronous FL. Taking the constrained resource and unreliable nature of wireless communication into account, we jointly study the user selection and bandwidth optimization problem to minimize the FL training loss. To solve this joint optimization problem, we provide a thorough convergence analysis for TT-Fed. Based on the obtained analytical convergence upper bound, the optimization problem is decomposed into tractable sub-problems with respect to each global aggregation round, and finally solved by our proposed online search algorithm. Simulation results show that compared to asynchronous FL (FedAsync) and FL with asynchronous user tiers (FedAT) benchmarks, our proposed TT-Fed algorithm improves the converged test accuracy by up to 12.5% and 5%, respectively, under highly imbalanced and non-IID data, while substantially reducing the communication overhead.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 28 of its 39 references that are in the map. First posted on arXiv 26 Apr 2022.

Builds on

28 of 39 references.

References · 28

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2019Advances and Open Problems in Federated Learningcited by 3,021
  3. 2021Convex optimizationcited by 2,479
  4. 2019Federated Learning: Challenges, Methods, and Future Directionscited by 1,531
  5. 2019On the Convergence of FedAvg on Non-IID Datacited by 1,220
  6. 2019Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classificationcited by 836
  7. 2019Robust and Communication-Efficient Federated Learning from Non-IID Datacited by 415
  8. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  9. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 407
  10. 2019Bayesian Nonparametric Federated Learning of Neural Networkscited by 385
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2019Asynchronous Federated Optimizationcited by 264
16 more
  1. 2019A Joint Learning and Communications Framework for Federated Learning over Wireless Networkscited by 262
  2. 2018Federated Learning via Over-the-Air Computationcited by 215
  3. 2021Federated Learning with Buffered Asynchronous Aggregationcited by 188
  4. 2019Energy Efficient Federated Learning Over Wireless Communication Networkscited by 181
  5. 2019Scheduling Policies for Federated Learning in Wireless Networkscited by 131
  6. 2020Client Selection and Bandwidth Allocation in Wireless Federated Learning Networks: A Long-Term Perspectivecited by 102
  7. 2020TiFL: A Tier-based Federated Learning Systemcited by 95
  8. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  9. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  10. 2020FedAT: A High-Performance and Communication-Efficient Federated Learning System with Asynchronous Tierscited by 64
  11. 2020Federated Learning in Unreliable and Resource-Constrained Cellular Wireless Networkscited by 29
  12. 2019Cell-Free Massive MIMO for Wireless Federated Learningcited by 25
  13. 2020Asynchronous Federated Learning with Reduced Number of Rounds and with Differential Privacy from Less Aggregated Gaussian Noisecited by 21
  14. 2021Adaptive Transmission Scheduling in Wireless Networks for Asynchronous Federated Learningcited by 15
  15. 2021Joint Client Scheduling and Resource Allocation under Channel Uncertainty in Federated Learningcited by 10
  16. 2019Asynchronous Online Federated Learning for Edge Devices with Non-IID Datacited by 9

Built on by

Cited by year

Citing papers per year: 2022: 1; 2023: 5; 2024: 2; 2025: 2.

10 papers.

  1. 2022Federated Learning and Meta Learning: Approaches, Applications, and Directionscited by 12
  2. 2023Joint Age-based Client Selection and Resource Allocation for Communication-Efficient Federated Learning over NOMA Networkscited by 8
  3. 2023Multi-Carrier NOMA-Empowered Wireless Federated Learning with Optimal Power and Bandwidth Allocationcited by 5
  4. 2024Joint Model Pruning and Resource Allocation for Wireless Time-triggered Federated Learningcited by 3
  5. 2023Towards Scalable Wireless Federated Learning: Challenges and Solutionscited by 1
  6. 2025TT-Prune: Joint Model Pruning and Resource Allocation for Communication-efficient Time-triggered Federated Learningcited by 1
  7. 2023Semi-Asynchronous Federated Edge Learning Mechanism via Over-the-air Computationcited by 0
  8. 2023Client Orchestration and Cost-Efficient Joint Optimization for NOMA-Enabled Hierarchical Federated Learningcited by 0
  9. 2024Robust Model Aggregation for Heterogeneous Federated Learning: Analysis and Optimizationscited by 0
  10. 2025Efficient UAV Swarm-Based Multi-Task Federated Learning with Dynamic Task Knowledge Sharingcited by 0