mlsanityData to 7 Oct 2026

Dynamic D2D-Assisted Federated Learning over O-RAN: Performance Analysis, MAC Scheduler, and Asymmetric User Selection

arXiv 2404.06324 ↗First posted 9 Apr 2024cs.NI cs.AI cs.LGcited by 3

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Abstract (arXiv)

Existing studies on federated learning (FL) are mostly focused on system orchestration for static snapshots of the network and making static control decisions (e.g., spectrum allocation). However, real-world wireless networks are susceptible to temporal variations of wireless channel capacity and users' datasets. In this paper, we incorporate multi-granular system dynamics (MSDs) into FL, including (M1) dynamic wireless channel capacity, captured by a set of discrete-time events, called mathscrD-Events, and (M2) dynamic datasets of users. The latter is characterized by (M2-a) modeling the dynamics of user's dataset size via an ordinary differential equation and (M2-b) introducing dynamic model drift, formulated via a partial differential inequality drawing concrete analytical connections between the dynamics of users' datasets and FL accuracy. We then conduct FL orchestration under MSDs by introducing dynamic cooperative FL with dedicated MAC schedulers (DCLM), exploiting the unique features of open radio access network (O-RAN). DCLM proposes (i) a hierarchical device-to-device (D2D)-assisted model training, (ii) dynamic control decisions through dedicated O-RAN MAC schedulers, and (iii) asymmetric user selection. We provide extensive theoretical analysis to study the convergence of DCLM. We then optimize the degrees of freedom (e.g., user selection and spectrum allocation) in DCLM through a highly non-convex optimization problem. We develop a systematic approach to obtain the solution for this problem, opening the door to solving a broad variety of network-aware FL optimization problems. We show the efficiency of DCLM via numerical simulations and provide a series of future directions.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 14 of its 45 references that are in the map. First posted on arXiv 9 Apr 2024.

Builds on

14 of 45 references.

References · 14

  1. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  2. 2021Model-Contrastive Federated Learningcited by 762
  3. 2016CVXPY: A Python-Embedded Modeling Language for Convex Optimizationcited by 633
  4. 2014Convex Optimization: Algorithms and Complexitycited by 569
  5. 2018Adaptive Federated Learning in Resource Constrained Edge Computing Systemscited by 408
  6. 2019Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocationcited by 93
  7. 2019Decentralized Federated Learning: A Segmented Gossip Approachcited by 63
  8. 2022Edge Learning for B5G Networks with Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensingcited by 54
  9. 2020Multi-Task Federated Learning for Personalised Deep Neural Networks in Edge Computingcited by 52
  10. 2022dApps: Distributed Applications for Real-time Inference and Control in O-RANcited by 17
  11. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
  12. 2022Online Federated Learning via Non-Stationary Detection and Adaptation amidst Concept Driftcited by 9
2 more
  1. 2023A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMOcited by 2
  2. 2023Elastic Federated Learning over Open Radio Access Network (O-RAN) for Concurrent Execution of Multiple Distributed Learning Taskscited by 1

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Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025pFedWN: A Personalized Federated Learning Framework for D2D Wireless Networks with Heterogeneous Datacited by 3
  2. 2025Multi-Modal Multi-Task Federated Foundation Models for Next-Generation Extended Reality Systems: Towards Privacy-Preserving Distributed Intelligence in AR/VR/MRcited by 2
  3. 2025From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walkscited by 0