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Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks

arXiv 2007.09511 ↗First posted 18 Jul 2020cs.NI cs.LGcited by 42

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Abstract (arXiv)

Federated learning has generated significant interest, with nearly all works focused on a "star" topology where nodes/devices are each connected to a central server. We migrate away from this architecture and extend it through the network dimension to the case where there are multiple layers of nodes between the end devices and the server. Specifically, we develop multi-stage hybrid federated learning (MH-FL), a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications, and presumes a semi-decentralized architecture for federated learning. It orchestrates the devices at different network layers in a collaborative/cooperative manner (i.e., using D2D interactions) to form local consensus on the model parameters and combines it with multi-stage parameter relaying between layers of the tree-shaped hierarchy. We derive the upper bound of convergence for MH-FL with respect to parameters of the network topology (e.g., the spectral radius) and the learning algorithm (e.g., the number of D2D rounds in different clusters). We obtain a set of policies for the D2D rounds at different clusters to guarantee either a finite optimality gap or convergence to the global optimum. We then develop a distributed control algorithm for MH-FL to tune the D2D rounds in each cluster over time to meet specific convergence criteria. Our experiments on real-world datasets verify our analytical results and demonstrate the advantages of MH-FL in terms of resource utilization metrics.

Key facts. Cited by 42 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Jul 2020.

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Citing papers per year: 2021: 10; 2022: 10; 2023: 9; 2024: 8; 2025: 4; 2026: 1.

42 papers.

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  1. 2021Distributed Learning in Wireless Networks: Recent Progress and Future Challengescited by 129
  2. 2021Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregationscited by 51
  3. 2021Device Sampling for Heterogeneous Federated Learning: Theory, Algorithms, and Implementationcited by 40
  4. 2023Federated Learning-Empowered AI-Generated Content in Wireless Networkscited by 26
  5. 2023Topology-aware Federated Learning in Edge Computing: A Comprehensive Surveycited by 23
  6. 2021UAV-assisted Online Machine Learning over Multi-Tiered Networks: A Hierarchical Nested Personalized Federated Learning Approachcited by 20
  7. 2022Parallel Successive Learning for Dynamic Distributed Model Training over Heterogeneous Wireless Networkscited by 18
  8. 2022Latency Optimization for Blockchain-Empowered Federated Learning in Multi-Server Edge Computingcited by 13
  9. 2023Filling the Missing: Exploring Generative AI for Enhanced Federated Learning over Heterogeneous Mobile Edge Devicescited by 13
  10. 2022Autonomy and Intelligence in the Computing Continuum: Challenges, Enablers, and Future Directions for Orchestrationcited by 12
  11. 2021Opportunistic Federated Learning: An Exploration of Egocentric Collaboration for Pervasive Computing Applicationscited by 11
  12. 2022Multi-Edge Server-Assisted Dynamic Federated Learning with an Optimized Floating Aggregation Pointcited by 10
30 more
  1. 2023Hierarchical Federated Learning in Wireless Networks: Pruning Tackles Bandwidth Scarcity and System Heterogeneitycited by 10
  2. 2022Decentralized Event-Triggered Federated Learning with Heterogeneous Communication Thresholdscited by 9
  3. 2022Non-Coherent Over-the-Air Decentralized Gradient Descentcited by 9
  4. 2023Delay-Aware Hierarchical Federated Learningcited by 9
  5. 2024Hierarchical Federated Learning with Multi-Timescale Gradient Correctioncited by 7
  6. 2021FedFog: Network-Aware Optimization of Federated Learning over Wireless Fog-Cloud Systemscited by 6
  7. 2025Federated Learning for Cyber Physical Systems: A Comprehensive Surveycited by 6
  8. 2021The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communicationcited by 5
  9. 2022Embedding Alignment for Unsupervised Federated Learning via Smart Data Exchangecited by 5
  10. 2022Social-Aware Clustered Federated Learning with Customized Privacy Preservationcited by 5
  11. 2024Analog-digital Scheduling for Federated Learning: A Communication-Efficient Approachcited by 5
  12. 2023A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learningcited by 3
  13. 2021Management of Resource at the Network Edge for Federated Learningcited by 2
  14. 2022On Differential Privacy for Federated Learning in Wireless Systems with Multiple Base Stationscited by 2
  15. 2024Biased Over-the-Air Federated Learning under Wireless Heterogeneitycited by 2
  16. 2024UAV-assisted Unbiased Hierarchical Federated Learning: Performance and Convergence Analysiscited by 2
  17. 2024Does Worst-Performing Agent Lead the Pack? Analyzing Agent Dynamics in Unified Distributed SGDcited by 2
  18. 2025Hierarchical Federated Foundation Models over Wireless Networks for Multi-Modal Multi-Task Intelligence: Integration of Edge Learning with D2D/P2P-Enabled Fog Learning Architecturescited by 2
  19. 2021Resource-Efficient and Delay-Aware Federated Learning Design under Edge Heterogeneitycited by 1
  20. 2023Federated Learning over Hierarchical Wireless Networks: Training Latency Minimization via Submodel Partitioningcited by 1
  21. 2024Sequential Federated Learning in Hierarchical Architecture on Non-IID Datasetscited by 1
  22. 2025The Transition from Centralized Machine Learning to Federated Learning for Mental Health in Education: A Survey of Current Methods and Future Directionscited by 1
  23. 2021Federated Learning Beyond the Star: Local D2D Model Consensus with Global Cluster Samplingcited by 0
  24. 2022Resource-Constrained Decentralized Federated Learning via Personalized Event-Triggeringcited by 0
  25. 2023FedDec: Peer-to-peer Aided Federated Learningcited by 0
  26. 2023Holonic Learning: A Flexible Agent-based Distributed Machine Learning Frameworkcited by 0
  27. 2024Unsupervised Federated Optimization at the Edge: D2D-Enabled Learning without Labelscited by 0
  28. 2024Twin Sorting Dynamic Programming Assisted User Association and Wireless Bandwidth Allocation for Hierarchical Federated Learningcited by 0
  29. 2025From Federated Learning to X-Learning: Breaking the Barriers of Decentrality Through Random Walkscited by 0
  30. 2026Joint Routing and Model Pruning for Decentralized Federated Learning in Bandwidth-Constrained Multi-Hop Wireless Networkscited by 0