mlsanityData to 7 Oct 2026

Asynchronous Multi-Model Dynamic Federated Learning over Wireless Networks: Theory, Modeling, and Optimization

arXiv 2305.13503 ↗First posted 22 May 2023cs.LG cs.DCcited by 3

Not on the map. Why?

Abstract (arXiv)

Federated learning (FL) has emerged as a key technique for distributed machine learning (ML). Most literature on FL has focused on ML model training for (i) a single task/model, with (ii) a synchronous scheme for updating model parameters, and (iii) a static data distribution setting across devices, which is often not realistic in practical wireless environments. To address this, we develop DMA-FL considering dynamic FL with multiple downstream tasks/models over an asynchronous model update architecture. We first characterize convergence via introducing scheduling tensors and rectangular functions to capture the impact of system parameters on learning performance. Our analysis sheds light on the joint impact of device training variables (e.g., number of local gradient descent steps), asynchronous scheduling decisions (i.e., when a device trains a task), and dynamic data drifts on the performance of ML training for different tasks. Leveraging these results, we formulate an optimization for jointly configuring resource allocation and device scheduling to strike an efficient trade-off between energy consumption and ML performance. Our solver for the resulting non-convex mixed integer program employs constraint relaxations and successive convex approximations with convergence guarantees. Through numerical experiments, we reveal that DMA-FL substantially improves the performance-efficiency tradeoff.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 40 of its 77 references that are in the map. First posted on arXiv 22 May 2023.

Builds on

40 of 77 references.

References · 40

  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. 2017Federated Multi-Task Learningcited by 669
  6. 2018Local SGD Converges Fast and Communicates Littlecited by 595
  7. 2021Federated Learning on Non-IID Data Silos: An Experimental Studycited by 566
  8. 2021Federated Learning Based on Dynamic Regularizationcited by 555
  9. 2019Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraintscited by 499
  10. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  11. 2018Client Selection for Federated Learning with Heterogeneous Resources in Mobile Edgecited by 347
  12. 2020Personalized Federated Learning: A Meta-Learning Approachcited by 309
28 more
  1. 2019Asynchronous Federated Optimizationcited by 264
  2. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  3. 2017Asynchronous Decentralized Parallel Stochastic Gradient Descentcited by 159
  4. 2013Communication Efficient Distributed Optimization using an Approximate Newton-type Methodcited by 131
  5. 2019Communication-Efficient Federated Deep Learning with Asynchronous Model Update and Temporally Weighted Aggregationcited by 99
  6. 2016Asynchronous Stochastic Gradient Descent with Delay Compensationcited by 98
  7. 2019Hierarchical Federated Learning Across Heterogeneous Cellular Networkscited by 98
  8. 2017On the convergence properties of a K-step averaging stochastic gradient descent algorithm for nonconvex optimizationcited by 91
  9. 2020Joint Device Scheduling and Resource Allocation for Latency Constrained Wireless Federated Learningcited by 90
  10. 2020Convergence of Update Aware Device Scheduling for Federated Learning at the Wireless Edgecited by 71
  11. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 50
  12. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  13. 2018First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problemscited by 33
  14. 2021Efficient Device Scheduling with Multi-Job Federated Learningcited by 29
  15. 2015Distributed Optimization with Arbitrary Local Solverscited by 23
  16. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  17. 2021Learning with Gradient Descent and Weakly Convex Lossescited by 16
  18. 2022Mitigating Biases in Student Performance Prediction via Attention-Based Personalized Federated Learningcited by 13
  19. 2020Dynamic Federated Learningcited by 12
  20. 2020Toward Multiple Federated Learning Services Resource Sharing in Mobile Edge Networkscited by 11
  21. 2016Parallel and Distributed Methods for Nonconvex Optimization--Part II: Applicationscited by 10
  22. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  23. 2023Delay-Aware Hierarchical Federated Learningcited by 9
  24. 2016Big Data Caching for Networking: Moving from Cloud to Edgecited by 8
  25. 2021Data-Aware Device Scheduling for Federated Edge Learningcited by 6
  26. 2021Successive Convex Approximation Based Off-Policy Optimization for Constrained Reinforcement Learningcited by 5
  27. 2021Resource-Aware Asynchronous Online Federated Learning for Nonlinear Regressioncited by 5
  28. 2023Distributionally Robust Learning with Weakly Convex Losses: Convergence Rates and Finite-Sample Guaranteescited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.

3 papers.

  1. 2023Decentralized Federated Learning: A Survey and Perspectivecited by 86
  2. 2025HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learningcited by 0
  3. 2026Optimizing Server Placement for Vertical Federated Learning in Dynamic Edge/Fog Networkscited by 0